论如何利用挖掘社交资讯来改进推荐系统
摘要
随着电子商务及移动商务的发展,推荐系统在研究领域和实践领域都显得越来越重要。传统的推荐系统研究者利用用户的打分列表作为推荐的基础,而忽略了能够影响用户偏好的其他因素。然而,实际上用户偏好以及他们的购买决策同时取决于他们自身的经验以及社交信息。以前也有推荐系统研究者在推断用户偏好和打分的时候考虑了社交信息,但是他们只集中在用户的行为(这会带来用户隐私问题)或利用用户之间的距离去计算用户之间的影响力。此项研究着重考察如何充分利用社交信息做更为精准的推荐服务。社交信息指的是从社交环境中获取的信息,不仅包括从亲密的朋友处得来的信息,也包括从大众点评中所获取的信息。根据从社会学,行为学以及推荐系统的研究成果所获得的理论基础,我们提出三种新型的推荐系统方法。在此项研究中的实验比较了现存的推荐方法(包括传统经典推荐算法,近期经典推荐算法以及以前的社交推荐算法),和此研究中所提出的三种新型推荐算法(包括考虑到从用户朋友网络中挖掘出的社交信息的推荐算法,考虑到从大众点评中挖掘出的社交信息的推荐算法,以及考虑到从用户朋友网络和大众点评中挖掘出的社交信息的推荐算法)的推荐准确性。实验结果表明此项研究中新提出的三项推荐算法能够比之前的推荐算法在准确性,覆盖率以及F-measure等指标中都有更好的表现。特别是当用户没有提供任何打分的情境下,此三种推荐算法是不可取代的,因为其它现存的推荐方法在这种情境下是完全无法工作的。我们也发现从用户朋友网络挖掘社交信息比从大众点评挖掘社交信息更能帮助推荐系统准确的预测用户偏好和用户打分。
With the development of e-commerce and mobile commerce, recommender system has been more and more important in both the research field and practice field. Traditional recommender system researchers make use of user rating lists as the basis of recommendation, with ignoring other factors which affect users' preferences. However, in fact, user preference and purchase decisions depend on both themselves experience and social information. Previous recommendation researches take social information into considerations when they estimate user preferences and user ratings, but they only focus on user behaviors which may bring the privacy problems or just make the distance between users to measure the strength of social influence between users. This research focuses on how to make full use of social information to make more accurate recommendation. Social information refers to information from social environment, which not only includes information from close friends of users, but also includes information from public comments. According to the theoretical foundations learnt from the social researches, behavior researches and recommendation researches, we propose three novel social recommendation methods. Field experiments described in this research compare the accuracy between existing recommendation methods (including the classical traditional recommendation methods, recent classical recommendation methods and previous social recommendation method), social recommendation method considering social information mined from friends, social recommendation method considering social information mined from public comments and social recommendation method considering social information mined from both friends and public comments. Experiment results show our proposed three social recommendation methods could achieve much higher performance on precision, recall and F-measure than previous recommendation methods. Especially when users do not provide any ratings to movies, the social recommendation methods even become irreplaceable as other tested recommendation methods could not work at all on this condition. We also find the social information mined from friends play a more important role than the social information mined from public comments for the estimation of user preferences and the estimation of t ratings.
With the development of e-commerce and mobile commerce, recommender system has been more and more important in both the research field and practice field. Traditional recommender system researchers make use of user rating lists as the basis of recommendation, with ignoring other factors which affect users' preferences. However, in fact, user preference and purchase decisions depend on both themselves experience and social information. Previous recommendation researches take social information into considerations when they estimate user preferences and user ratings, but they only focus on user behaviors which may bring the privacy problems or just make the distance between users to measure the strength of social influence between users. This research focuses on how to make full use of social information to make more accurate recommendation. Social information refers to information from social environment, which not only includes information from close friends of users, but also includes information from public comments. According to the theoretical foundations learnt from the social researches, behavior researches and recommendation researches, we propose three novel social recommendation methods. Field experiments described in this research compare the accuracy between existing recommendation methods (including the classical traditional recommendation methods, recent classical recommendation methods and previous social recommendation method), social recommendation method considering social information mined from friends, social recommendation method considering social information mined from public comments and social recommendation method considering social information mined from both friends and public comments. Experiment results show our proposed three social recommendation methods could achieve much higher performance on precision, recall and F-measure than previous recommendation methods. Especially when users do not provide any ratings to movies, the social recommendation methods even become irreplaceable as other tested recommendation methods could not work at all on this condition. We also find the social information mined from friends play a more important role than the social information mined from public comments for the estimation of user preferences and the estimation of t ratings.
引文
[1]Rossi P E, McCulloch R E, Allenby G M. The value of purchase history data in target marketing[J]. Marketing Science,1996,15(4):321-340.
[2]Smith N C, Cooper-Martin E. Ethics and target marketing:The role of product harm and consumer vulnerability[J]. The Journal of Marketing,1997:1-20.
[3]Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J]. Knowledge and Data Engineering, IEEE Transactions on,2005,17(6):734-749.
[4]Salton, G. Automatic Text Processing. Addison-Wesley,1989.
[5]Statnikov, R. B. and J. B. Matusov. Multicriteria Optimization and Engineering. Chapman & Hall,1995.
[6]Billsus, D. and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction,10(2-3):147-180,2000.
[7]Marlin, B. Modeling User Rating Profiles for Collaborative Filtering. In Proceedings of the 17th Annual Conference on Neural Information Processing Systems (NIPS'03),2003.
[8]Ma H, Yang H, Lyu M R, et al. Sorec:social recommendation using probabilistic matrix factorization[C]. Proceedings of the 17th ACM conference on Information and knowledge management. ACM,2008:931-940.
[9]Walter F E, Battiston S, Schweitzer F. A model of a trust-based recommendation system on a social network[J]. Autonomous Agents and Multi-Agent Systems,2008,16(1):57-74.
[10]Debnath S, Ganguly N, Mitra P. Feature weighting in content based recommendation system using social network analysis[C]. Proceedings of the 17th international conference on World Wide Web. ACM,2008:1041-1042.
[11]Xiao B, Benbasat 1. E-commerce product recommendation agents:use, characteristics, and impact[J]. Mis Quarterly,2007,31(1):137-209.
[12]Swaminathan V. The impact of recommendation agents on consumer evaluation and choice: the moderating role of category risk, product complexity, and consumer knowledge[J]. Journal of Consumer Psychology,2003,13(1):93-101.
[13]Kramer T. The effect of measurement task transparency on preference construction and evaluations of personalized recommendations[J]. Journal of Marketing Research,2007:224-233.
[14]Yang F, WANG Z. A mobile location-based information recommendation system based on GPS and WEB2.0 services[J]. database,2009,7:8.
[15]Tung H W, Soo V W. A personalized restaurant recommender agent for mobile e-service[C] e-Technology, e-Commerce and e-Service,2004. EEE'04.2004 IEEE International Conference on. IEEE,2004:259-262.
[16]Yang W S, Cheng H C, Dia J B. A location-aware recommender system for mobile shopping environments[J]. Expert Systems with Applications,2008,34(1):437-445.
[17]Wasserman S, Faust K. Social network analysis:Methods and applications[M]. Cambridge university press,1994.
[18]Wellman B. For a social network analysis of computer networks:a sociological perspective on collaborative work and virtual community[C]. Proceedings of the 1996 ACM SIGCPR/SIGMIS conference on Computer personnel research. ACM,1996:1-11.
[19]Witt R E, Bruce G D. Group influence and brand choice congruence[J]. Journal of Marketing Research,1972,9(4):440-443.
[20]Bearden W O, Etzel M J. Reference group influence on product and brand purchase decisions[J]. Journal of Consumer research,1982:183-194.
[21]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[22]Massa P, Avesani P. Trust-aware recommender systems[C]. Proceedings of the 2007 ACM conference on Recommender systems. ACM,2007:17-24.
[23]Massa P, Bhattacharjee B. Using trust in recommender systems:an experimental analysis[M].Trust Management. Springer Berlin Heidelberg,2004:221-235.
[24]Golbeck J. Generating predictive movie recommendations from trust in social nerworks[M]. Springer Berlin Heidelberg,2006.
[25]Carmagnola F, Vemero F, Grillo P. Sonars:A social networks-based algorithm for social recommender systems[M]. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg,2009:223-234.
[26]Jamali M, Haffari G, Ester M. Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns[C]. Proceedings of the 20th international conference on World wide web. ACM,2011:527-536.
[27]Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms[C]. Proceedings of the 10th international conference on World Wide Web. ACM,2001: 285-295.
[28]Ma H, Zhou D, Liu C, et al. Recommender systems with social regularization[C]. Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011:287-296.
[29]Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer,2009,42(8):30-37.
[30]Kordumova S, Kostadinovska I, Barbieri M, et al. Personalized implicit learning in a music recommender system[M]. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg,2010:351-362.
[31]Nunez-Valdez E R, Cueva Lovelle J M, Sanjuan Martinez O, et al. Implicit feedback techniques on recommender systems applied to electronic books[J]. Computers in Human Behavior,2012.
[32]Kim J W, Lee B H, Shaw M J, et al. Application of decision-tree induction techniques to personalized advertisements on internet storefronts[J]. International Journal of Electronic Commerce,2001,5:45-62.
[33]Billsus D, Pazzani M J, Chen J. A learning agent for wireless news access[C]. Proceedings of the 5th international conference on Intelligent user interfaces. ACM,2000:33-36.
[34]Pazzani M, Billsus D. Learning and revising user profiles:The identification of interesting web sites[J]. Machine learning,1997,27(3):313-331.
[35]Pazzani M J, Billsus D. Content-based recommendation systems[M].The adaptive web. Springer Berlin Heidelberg,2007:325-341.
[36]Maltz D, Ehrlich K. Pointing the way:active collaborative filtering[C]. Proceedings of the SIGCHI conference on Human factors in computing systems. ACM Press/Addison-Wesley Publishing Co.,1995:202-209.
[37]Linden G, Smith B, York J. Amazon, com recommendations:Item-to-item collaborative filtering[J]. Internet Computing, IEEE,2003,7(1):76-80.
[38]Konstan J A, Miller B N, Maltz D, et al. GroupLens:applying collaborative filtering to Usenet news[J]. Communications of the ACM,1997,40(3):77-87.
[39]Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.,1998:43-52.
[40]Yoshii K, Goto M, Komatani K, et al. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model[J]. Audio, Speech, and Language Processing, IEEE Transactions on,2008,16(2):435-447.
[41]Li Q, Kim B M. An approach for combining content-based and collaborative filters[C]. Proceedings of the sixth international workshop on Information retrieval with Asian languages-Volume 11. Association for Computational Linguistics,2003:17-24.
[42]Popescul A, Pennock D M, Lawrence S. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments[C]. Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.,2001: 437-444.
[43]Mobasher B, Cooley R, Srivastava J. Automatic personalization based on Web usage mining[J]. Communications of the ACM,2000,43(8):142-151.
[44]Cho Y H, Kim J K. Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce[J]. Expert systems with Applications,2004,26(2):233-246.
[45]Mobasher B, Cooley R, Srivastava J. Automatic personalization based on Web usage mining[J]. Communications of the ACM,2000,43(8):142-151.
[46]Middleton S E, Shadbolt N R, De Roure D C. Ontological user profiling in recommender systems[J]. ACM Transactions on Information Systems (TOIS),2004,22(1):54-88.
[47]Zhang Y, Koren J. Efficient bayesian hierarchical user modeling for recommendation system[C]. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2007:47-54.
[48]Salton G. Automatic text processing[J]. Science,1970,168(3929):335-343.
[49]Phelan O, McCarthy K, Smyth B. Using twitter to recommend real-time topical news[C]. Proceedings of the third ACM conference on Recommender systems. ACM,2009:385-388.
[50]Niwa S, Honiden S. Web page recommender system based on folksonomy mining for itng'06 submissions[C]. Information Technology: New Generations,2006. ITNG 2006. Third International Conference on. IEEE,2006:388-393.
[51]Costa A, Guizzardi R, Guizzardi Filho G. J.:COReS:Context-aware, Ontology-based Recommender system for Service recommendation[C]. Proceedings of the 19th international conference on advanced information systems engineering (CAiSE'07).
[52]Park J H, Park W I, Kim Y K, et al. Personalized recommender system for resource sharing based on context-aware in ubiquitous environments[C]. Information Technology,2008. ITSim 2008. International Symposium on. IEEE,2008,4:1-5.
[53]Jain A K, Murty M N, Flynn P J. Data clustering: a review[J]. ACM computing surveys (CSUR),1999,31(3):264-323.
[54]George T, Merugu S. A scalable collaborative filtering framework based on co-clustering[C]. Data Mining, Fifth IEEE International Conference on. IEEE,2005:4 pp.
[55]Blanco-Femandez Y, Pazos-Arias J J, Gil-Solla A, et al. A multi-agent open architecture for a TV recommender system:a case study using a Bayesian strategy[C]. Multimedia Software Engineering,2004. Proceedings. IEEE Sixth International Symposium on. IEEE,2004:178-185.
[56]Quinlan J R. Induction of decision trees[J]. Machine learning,1986,1(1):81-106.
[57]Cho Y H, Kim J K, Kim S H. A personalized recommender system based on web usage mining and decision tree induction[J]. Expert Systems with Applications,2002,23(3):329-342.
[58]Golbandi N, Koren Y, Lempel R. Adaptive bootstrapping of recommender systems using decision trees[C]. Proceedings of the fourth ACM international conference on Web search and data mining. ACM,2011:595-604.
[59]Dayhoff J E, DeLeo J M. Artificial neural networks[J]. Cancer,2001,91(S8):1615-1635.
[60]Christakou C, Vrettos S, Stafylopatis A. A hybrid movie recommender system based on neural networks[J]. International Journal on Artificial Intelligence Tools,2007,16(05):771-792.
[61]Kurkovsky S, Harihar K. Using ubiquitous computing in interactive mobile marketing[J]. Personal and Ubiquitous Computing,2006,10(4):227-240.
[62]Kurkovsky S, Harihar K. Using ubiquitous computing in interactive mobile marketing[J]. Personal and Ubiquitous Computing,2006,10(4):227-240.
[63]De Pessemier T, Deryckere T, Martens L. Extending the Bayesian classifier to a context-aware recommender system for mobile devices[C]. Internet and Web Applications and Services (ICIW),2010 Fifth International Conference on. IEEE,2010:242-247.
[64]Fano A E. Shopper's eye:using location-based filtering for a shopping agent in the physical world[C]. Proceedings of the second international conference on Autonomous agents. ACM,1998: 416-421.
[65]Kawashima H, Matsushita T, Satake S, et al. PORSCHE:A physical objects recommender system for cell phone users[C]. Proceedings of the Second International Workshop on Personalized Context Modeling and Management for UbiComp Applications, Orange County, California.2006.
[66]Bohnert F. Adaptive user modelling and recommendation in constrained physical environments[C]. Adaptive Hypermedia and Adaptive Web-Based Systems. Springer Berlin Heidelberg,2008:378-383.
[67]Massa P, Avesani P. Trust-aware collaborative filtering for recommender systems[M]. On the Move to Meaningful Internet Systems 2004:CoopIS, DOA, and ODBASE. Springer Berlin Heidelberg,2004:492-508.
[68]Moghaddam S, Jamali M, Ester M, et al. FeedbackTrust:using feedback effects in trust-based recommendation systems[C]. Proceedings of the third ACM conference on Recommender systems. ACM,2009:269-272.
[69]Liu H, Maes P. Interestmap:Harvesting social network profiles for recommendations[J]. Beyond Personalization-IUI,2005.
[70]Crandall D, Cosley D, Huttenlocher D, et al. Feedback effects between similarity and social influence in online communities[C]. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2008:160-168.
[71]Bonhard P, Sasse M A.'Knowing me, knowing you'—sing profiles and social networking to improve recommender systems[J]. BT Technology Journal,2006,24(3):84-98.
[72]Yang W S, Dia J B. Discovering cohesive subgroups from social networks for targeted advertising[J]. Expert Systems with Applications,2008,34(3):2029-2038.
[73]Mudambi S M, Schuff D. What makes a helpful online review? A study of customer reviews on Amazon. com[J]. Mis Quarterly,2010,34(1):185-200.
[74]Awad N F, Ragowsky A. Establishing trust in electronic commerce through online word of mouth:an examination across genders[J]. Journal of Management Information Systems,2008, 24(4):101-121.
[75]Jansen B J, Zhang M, Sobel K, et al. Twitter power:Tweets as electronic word of mouth[J]. Journal of the American society for information science and technology,2009,60(11):2169-2188.
[76]Peres R, Van den Bulte C. How customer word of mouth affects the benefits of new product exclusivity to distributors[R]. Working Paper,2010.
[77]Zhu F, Zhang X. Impact of online consumer reviews on sales:The moderating role of product and consumer characteristics[J]. Journal of Marketing,2010,74(2):133-148.
[78]Vermeulen I E, Seegers D. Tried and tested:The impact of online hotel reviews on consumer consideration[J]. Tourism Management,2009,30(1):123-127.
[79]Lee T Q, Park Y, Park Y T. A time-based approach to effective recommender systems using implicit feedback[J]. Expert systems with applications,2008,34(4):3055-3062.
[80]Oard D W, Kim J. Implicit feedback for recommender systems[C]. Proceedings of the AAAI workshop on recommender systems. Wollongong,1998:81-83.
[81]Kelly D, Belkin N J. Reading time, scrolling and interaction:exploring implicit sources of user preferences for relevance feedback[C]. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2001:408-409.
[82]Jawaheer G, Szomszor M, Kostkova P. Comparison of implicit and explicit feedback from an online music recommendation service[C]. Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems. ACM,2010:47-51.
[83]Shahabi C, Chen Y S. An adaptive recommendation system without explicit acquisition of user relevance feedback[J]. Distributed and Parallel Databases,2003,14(2):173-192.
[84]Kim J, Oard D W, Romanik K. Using implicit feedback for user modeling in internet and intranet searching[J].2000.
[85]Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets[C].Data Mining,2008. ICDM'08. Eighth IEEE International Conference on IEEE,2008:263-272.
[86]Recker M M, Walker A. Supporting "Word-of-Mouth" social networks through collaborative information filtering[J]. Journal of Interactive Learning Research,2003,14(1):79-98.
[87]Webster A, Vassileva J. Push-Poll recommender system:supporting word of mouth[M]. User Modeling 2007. Springer Berlin Heidelberg,2007:278-287.
[88]Li F, Du T C. Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs[J]. Decision Support Systems,2011,51(1): 190-197.
[89]Heath T, Motta E, Petre M. Person to person trust factors in word of mouth recommendation[J].2006.
[90]Heath T, Motta E, Petre M. Computing word-of-mouth trust relationships in social networks from semantic web and web 2.0 data sources[J].2007.
[91]Kim Y, Srivastava J. Impact of social influence in e-commerce decision making[C]. Proceedings of the ninth international conference on Electronic commerce. ACM,2007:293-302.
[92]Arazy O, Kumar N, Shapira B. A theory-driven design framework for social recommender systems[J]. Journal of the Association for Information Systems,2010,11(9):455-490.
[93]Raven B H. Social influence and power[R]. CALIFORNIA UNIV LOS ANGELES,1964.
[94]Turner J C. Social influence[M]. Milton Keynes:Open University Press,1991.
[95]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[96]Festinger L. A theory of social comparison processes[J]. Human relations,1954,7(2): 117-140.
[97]Zajonc R B. Social fecilitation[M]. Research Center for Group Dynamics, Institute for Social Research, University of Michigan,1965.
[98]Bumkrant R E, Cousineau A. Informational and normative social influence in buyer behavior[J]. Journal of Consumer Research,1975:206-215.
[99]Festinger L. Informal social communication[J]. Psychological review,1950,57(5):271.
[100]Mullen B, Copper C. The relation between group cohesiveness and performance:An integration[R]. SYRACUSE UNIV NY,1995.
[101]Witt R E. Informal social group influence on consumer brand choice[J]. Journal of Marketing Research,1969:473-476.
[102]Moschis G P. Social comparison and informal group influence[J]. Journal of Marketing Research,1976:237-244.
[103]Stafford J E. Effects of group influences on consumer brand preferences[J]. Journal of marketing Research,1966:68-75.
[104]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[105]Kelman H C. Processes of opinion change[J]. The Planning of Change,1969:470-496.
[106]Park C W, Lessig V P. Students and housewives:Differences in susceptibility to reference group influence[J]. Journal of Consumer Research,1977:102-110.
[107]Kaplan M F, Miller C E. Group decision making and normative versus informational influence:Effects of type of issue and assigned decision rule[J]. Journal of Personality and Social Psychology,1987,53(2):306.
[108]Mangleburg T F, Doney P M, Bristol T. Shopping with friends and teens' susceptibility to peer influence[J]. Journal of Retailing,2004,80(2):101-116.
[109]Kelman H C. Processes of opinion change[J]. Public opinion quarterly,1961,25(1):57-78.
[110]Leskovec J, Lang K J, Dasgupta A, et al. Community structure in large networks:Natural cluster sizes and the absence of large well-defined clusters[J]. Internet Mathematics,2009,6(1): 29-123.
[111]Brown J J, Reingen P H. Social ties and word-of-mouth referral behavior[J], Journal of Consumer Research,1987:350-362.
[112]McPherson J M, Smith-Lovin L. Homophily in voluntary organizations:Status distance and the composition of face-to-face groups[J]. American sociological review,1987:370-379.
[113]Granovetter M S. The strength of weak ties[J]. American journal of sociology,1973: 1360-1380.
[114]Brown J, Broderick A J, Lee N. Word of mouth communication within online communities: Conceptualizing the online social network[J]. Journal of interactive marketing,2007,21(3):2-20.
[115]Travers J, Milgram S. An experimental study of the small world problem [J]. Sociometry, 1969:425-443.
[116]Onnela J P, Saramaki J, Hyvonen J, et al. Structure and tie strengths in mobile communication networks[J]. Proceedings of the National Academy of Sciences,2007,104(18): 7332-7336.
[117]Granovetter M. The strength of weak ties: A network theory revisited[J]. Sociological theory, 1983,1(1):201-233.
[118]Holes S. The Social Structure of Competition[J].1992.
[119]Scott J. Social Network Analysis:A Handbook[M]. SAGE Publications Limited,2000.
[120]Haythomthwaite C. Social network analysis:An approach and technique for the study of information exchange[J]. Library & Information Science Research,1996,18(4):323-342.
[121]Morrison E W. NEWCOMERS' RELATIONSHIPS:THE ROLE OF SOCIAL NETWORK TIES DURING SOCIALIZATION[J]. Academy of management Journal,2002,45(6):1149-1160.
[122]Lewis K, Kaufman J, Gonzalez M, et al. Tastes, ties, and time:A new social network dataset using Facebook. com[J]. Social Networks,2008,30(4):330-342.
[123]Laumann E O. Bonds of pluralism:The form and substance of urban social networks[M]. J. Wiley,1973.
[124]Campbell K E, Marsden P V, Hurlbert J S. Social resources and socioeconomic status[J]. Social networks,1986,8(1):97-117.
[125]Marsden P V. Core discussion networks of Americans[J]. American sociological review, 1987:122-131.
[126]Daly E M, Haahr M. Social network analysis for routing in disconnected delay-tolerant manets[C]. Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing. ACM,2007:32-40.
[127]Friedkin N E. Structural bases of interpersonal influence in groups:A longitudinal case study[J]. American Sociological Review,1993:861-872.
[128]Ibarra H, Andrews S B. Power, social influence, and sense making:Effects of network centrality and proximity on employee perceptions[J]. Administrative science quarterly,1993: 277-303.
[129]Sparrowe R T, Liden R C, Wayne S J, et al. Social Networks and the Performance of Individuals and Groups[J]. Academy of management journal,2001,44(2):316-325.
[130]Cook K S, Emerson R M. Power, equity and commitment in exchange networks[J]. American Sociological Review,1978:721-739.
[131]Brass D J. Being in the right place:A structural analysis of individual influence in an organization[J]. Administrative Science Quarterly,1984:518-539.
[132]Mathieson K. Predicting user intentions:comparing the technology acceptance model with the theory of planned behavior[J]. Information systems research,1991,2(3):173-191.
[133]Venkatesh V, Davis F D. A theoretical extension of the technology acceptance model:four longitudinal field studies[J]. Management science,2000,46(2):186-204.
[134]Lee S K, Cho Y H, Kim S H. Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations[J]. Information Sciences,2010,180(11):2142-2155.
[135]HUANG, Zhihua, Songnian LUa, and Aixin ZHANG. Implicit Rating Schemes Based on Observable Behaviors in P2P Trust Systems[J].2012.
[136]DeCoste D. Collaborative prediction using ensembles of maximum margin matrix factorizations[C]. Proceedings of the 23rd international conference on Machine learning. ACM, 2006:249-256.
[137]Miyahara K, Pazzani M J. Collaborative filtering with the simple Bayesian classifier[M]. PRICAI 2000 Topics in Artificial Intelligence. Springer Berlin Heidelberg,2000:679-689.
[138]Carterette B, Bennett P N. Evaluation measures for preference judgments[C]. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2008:685-686.
[139]Ellison N B. Social network sites:Definition, history, and scholarship[J]. Journal of Computer-Mediated Communication,2007,13(1):210-230.
[1]Rossi P E, McCulloch R E, Allenby G M. The value of purchase history data in target marketing[J]. Marketing Science,1996,15(4):321-340.
[2]Smith N C, Cooper-Martin E. Ethics and target marketing:The role of product harm and consumer vulnerability [J]. The Journal of Marketing,1997:1-20.
[3]Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J]. Knowledge and Data Engineering, IEEE Transactions on,2005,17(6):734-749.
[4]Salton, G. Automatic Text Processing. Addison-Wesley,1989.
[5]Stamikov, R B. and J. B. Matusov. Multicriteria Optimization and Engineering. Chapman & Hall,1995.
[6]Billsus, D. and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction,10(2-3):147-180,2000.
[7]Marlin, B. Modeling User Rating Profiles for Collaborative Filtering. In Proceedings of the 17th Annual Conference on Neural Information Processing Systems (NIPS'03),2003.
[8]Ma H, Yang H, Lyu M R, et al. Sorec:social recommendation using probabilistic matrix factorization[C]. Proceedings of the 17th ACM conference on Information and knowledge management. ACM,2008:931-940.
[9]Walter F E, Battiston S, Schweitzer F. A model of a trust-based recommendation system on a social network[J]. Autonomous Agents and Multi-Agent Systems,2008,16(1):57-74.
[10]Debnath S, Ganguly N, Mitra P. Feature weighting in content based recommendation system using social network analysis[C]. Proceedings of the 17th international conference on World Wide Web. ACM,2008:1041-1042.
[11]Xiao B, Benbasat I. E-commerce product recommendation agents:use, characteristics, and impact[J]. Mis Quarterly,2007,31(1):137-209.
[12]Swaminathan V. The impact of recommendation agents on consumer evaluation and choice: the moderating role of category risk, product complexity, and consumer knowledge[J]. Journal of Consumer Psychology,2003,13(1):93-101.
[13]Kramer T. The effect of measurement task transparency on preference construction and evaluations of personalized recommendations[J]. Journal of Marketing Research,2007: 224-233.
[14]Yang F, WANG Z. A mobile location-based information recommendation system based on GPS and WEB2.0 services[J]. database,2009,7:8.
[15]Tung H W, Soo V W. A personalized restaurant recommender agent for mobile e-service[C] e-Technology, e-Commerce and e-Service,2004. EEE'04.2004 IEEE International Conference on. IEEE,2004:259-262.
[16]Yang W S, Cheng H C, Dia J B. A location-aware recommender system for mobile shopping environments[J]. Expert Systems with Applications,2008,34(1):437-445.
[17]Wasserman S, Faust K. Social network analysis:Methods and applications[M]. Cambridge university press,1994.
[18]Wellman B. For a social network analysis of computer networks:a sociological perspective on collaborative work and virtual community[C]. Proceedings of the 1996 ACM SIGCPR/SIGMIS conference on Computer personnel research. ACM,1996:1-11.
[19]Massa P, Avesani P. Trust-aware recommender systems[C]. Proceedings of the 2007 ACM conference on Recommender systems. ACM,2007:17-24.
[20]Massa P, Bhattacharjee B. Using trust in recommender systems:an experimental analysis[M].Trust Management. Springer Berlin Heidelberg,2004:221-235.
[21]Golbeck J. Generating predictive movie recommendations from trust in social networks[M]. Springer Berlin Heidelberg,2006.
[22]Carmagnola F, Vernero F, Grillo P. Sonars:A social networks-based algorithm for social recommender systems[M]. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg,2009:223-234.
[23]Jamah" M, Haffari G, Ester M. Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating pattems[C]. Proceedings of the 20th international conference on World wide web. ACM,2011:527-536.
[24]Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms[C]. Proceedings of the 10th international conference on World Wide Web. ACM, 2001:285-295.
[25]Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer,2009,42(8):30-37.
[26]Kordumova S, Kostadinovska I, Barbieri M, et al. Personalized implicit learning in a music recommender system[M]. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg,2010:351-362.
[27]Nunez-Valdez E R, Cueva Lovelle J M, Sanjuan Martinez O, et al. Implicit feedback techniques on recommender systems applied to electronic books[J]. Computers in Human Behavior,2012.
[28]Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.,1998:43-52.
[29]Salton G. Automatic text processing[J]. Science,1970,168(3929):335-343.
[30]Phelan O, McCarthy K, Smyth B. Using twitter to recommend real-time topical news[C]. Proceedings of the third ACM conference on Recommender systems. ACM,2009:385-388.
[31]Niwa S, Honiden S. Web page recommender system based on folksonomy mining for itng'06 submissions[C]. Information Technology:New Generations,2006. ITNG 2006. Third International Conference on. IEEE,2006:388-393.
[32]Jain A K, Murty M N, Flynn P J. Data clustering:a review[J]. ACM computing surveys (CSUR),1999,31(3):264-323.
[33]George T, Merugu S. A scalable collaborative filtering framework based on co-clustering[C]. Data Mining, Fifth IEEE International Conference on. IEEE,2005:4 pp.
[34]Blanco-Fernandez Y, Pazos-Arias J J, Gil-Solla A, et al. A multi-agent open architecture for a TV recommender system:a case study using a Bayesian strategy[C]. Multimedia Software Engineering,2004. Proceedings. IEEE Sixth International Symposium on. IEEE,2004: 178-185.
[35]Quinlan J R. Induction of decision trees[J]. Machine learning,1986,1(1):81-106.
[36]Cho Y H, Kim J K, Kim S H. A personalized recommender system based on web usage mining and decision tree induction[J]. Expert Systems with Applications,2002.23(3): 329-342.
[37]Golbandi N, Koren Y, Lempel R. Adaptive bootstrapping of recommender systems using decision trees[C]. Proceedings of the fourth ACM international conference on Web search and data mining. ACM,2011:595-604.
[38]Dayhoff J E, DeLeo J M. Artificial neural networks[J]. Cancer,2001,91(S8):1615-1635.
[39]Christakou C, Vrettos S, Stafylopatis A. A hybrid movie recommender system based on neural networks[J]. International Journal on Artificial Intelligence Tools,2007,16(05): 771-792.
[40]Massa P, Avesani P. Trust-aware collaborative filtering for recommender systems[M]. On the Move to Meaningful Internet Systems 2004:CoopIS, DOA, and ODBASE. Springer Berlin Heidelberg,2004:492-508.
[41]Crandall D, Cosley D, Huttenlocher D, et al. Feedback effects between similarity and social influence in online communities[C]. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2008:160-168.
[42]Mudambi S M, Schuff D. What makes a helpful online review? A study of customer reviews on Amazon. com[J]. Mis Quarterly,2010,34(1):185-200.
[43]Awad N F, Ragowsky A. Establishing trust in electronic commerce through online word of mouth:an examination across genders[J]. Journal of Management Information Systems,2008, 24(4):101-121.
[44]Jansen B J, Zhang M, Sobel K, et al. Twitter power:Tweets as electronic word of mouth[J]. Journal of the American society for information science and technology,2009, 60(11):2169-2188.
[45]Peres R, Van den Bulte C. How customer word of mouth affects the benefits of new product exclusivity to distributors[R]. Working Paper,2010.
[46]Zhu F, Zhang X. Impact of online consumer reviews on sales:The moderating role of product and consumer characteristics[J]. Journal of Marketing,2010,74(2):133-148.
[47]Vermeulen I E, Seegers D. Tried and tested:The impact of online hotel reviews on consumer consideration[J]. Tourism Management,2009,30(1):123-127.
[48]Lee T Q, Park Y, Park Y T. A time-based approach to effective recommender systems using implicit feedback[J]. Expert systems with applications,2008,34(4):3055-3062.
[49]Oard D W, Kim J. Implicit feedback for recommender systems[C]. Proceedings of the AAAI workshop on recommender systems. Wollongong,1998:81-83.
[50]Kelly D, Belkin N J. Reading time, scrolling and interaction:exploring implicit sources of user preferences for relevance feedback[C]. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2001: 408-409.
[51]Jawaheer G, Szomszor M, Kostkova P. Comparison of implicit and explicit feedback from an online music recommendation service[C]. Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems. ACM,2010:47-51.
[52]Shahabi C, Chen Y S. An adaptive recommendation system without explicit acquisition of user relevance feedback[J]. Distributed and Parallel Databases,2003,14(2):173-192.
[53]Kim J, Oard D W, Romanik K. Using implicit feedback for user modeling in internet and intranet searching[J].2000.
[54]Recker M M, Walker A. Supporting "Word-of-Mouth" social networks through collaborative information filtering[J]. Journal of Interactive Learning Research,2003,14(1): 79-98.
[55]Webster A, Vassileva J. Push-Poll recommender system:supporting word of mouth[M]. User Modeling 2007. Springer Berlin Heidelberg,2007:278-287.
[56]Li F. Du T C. Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs[J]. Decision Support Systems,2011, 51(1):190-197.
[57]Heath T, Motta E, Petre M. Person to person trust factors in word of mouth recommendation[J].2006.
[58]Heath T, Motta E, Petre M. Computing word-of-mouth trust relationships in social networks from semantic web and web 2.0 data sources[J].2007.
[59]Kim Y, Srivastava J. Impact of social influence in e-commerce decision making[C]. Proceedings of the ninth international conference on Electronic commerce. ACM,2007: 293-302.
[60]Arazy O, Kumar N, Shapira B. A theory-driven design framework for social recommender systems[J]. Journal of the Association for Information Systems,2010.11(9):455-490.
[61]Turner J C. Social influence[M]. Milton Keynes:Open University Press,1991.
[62]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[63]Festinger L. A theory of social comparison processes[J]. Human relations,1954,7(2): 117-140.
[64]Zajonc R B. Social facilitation[M]. Research Center for Group Dynamics, Institute for Social Research, University of Michigan,1965.
[65]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[66]Kelman H C. Processes of opinion change[J]. The Planning of Change,1969:470-496.
[67]Park C W, Lessig V P. Students and housewives:Differences in susceptibility to reference group influence[J]. Journal of Consumer Research,1977:102-110.
[68]Leskovec J, Lang K J, Dasgupta A, et al. Community structure in large networks:Natural cluster sizes and the absence of large well-defined clusters[J]. Internet Mathematics,2009, 6(1):29-123.
[69]Brown J J, Reingen P H. Social ties and word-of-mouth referral behavior[J]. Journal of Consumer Research,1987:350-362.
[70]McPherson J M, Smith-Lovin L. Homophily in voluntary organizations:Status distance and the composition of face-to-face groups[J]. American sociological review,1987:370-379.
[71]Granovetter M S. The strength of weak ties[J]. American journal of sociology,1973: 1360-1380.
[72]Brown J, Broderick A J, Lee N. Word of mouth communication within online communities: Conceptualizing the online social network[J]. Journal of interactive marketing,2007.21(3): 2-20.
[73]Holes S. The Social Structure of Competition[J].1992.
[74]Scott J. Social Network Analysis:A Handbook[M]. SAGE Publications Limited,2000.
[75]Haythornthwaite C. Social network analysis:An approach and technique for the study of information exchange[J]. Library & Information Science Research,1996,18(4):323-342.
[76]Morrison E W. NEWCOMERS'RELATIONSHIPS:THE ROLE OF SOCIAL NETWORK TIES DURING SOCIALIZATION[J]. Academy of management Journal,2002,45(6): 1149-1160.
[77]Lewis K, Kaufman J, Gonzalez M, et al. Tastes, ties, and time:A new social network dataset using Facebook. com[J]. Social Networks,2008,30(4):330-342.
[78]Laumann E O. Bonds of pluralism:The form and substance of urban social networks[M]. J. Wiley,1973.
[79]Campbell K E, Marsden P V, Hurlbert J S. Social resources and socioeconomic status[J]. Social networks,1986,8(1):97-117.
[80]Marsden P V. Core discussion networks of Americans[J]. American sociological review, 1987:122-131.
[81]Daly E M, Haahr M. Social network analysis for routing in disconnected delay-tolerant manets[C]. Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing. ACM,2007:32-40.
[82]Friedkin N E. Structural bases of interpersonal influence in groups: A longitudinal case study[J]. American Sociological Review,1993:861-872.
[83]Ibarra H, Andrews S B. Power, social influence, and sense making:Effects of network centrality and proximity on employee perceptions[J]. Administrative science quarterly,1993: 277-303.
[84]Sparrowe R T, Liden R C, Wayne S J, et al. Social Networks and the Performance of Individuals and Groups[J]. Academy of management journal,2001,44(2):316-325.
[85]Cook K S, Emerson R M. Power, equity and commitment in exchange networks[J]. American Sociological Review,1978:721-739.
[86]Brass D J. Being in the right place:A structural analysis of individual influence in an organization[J]. Administrative Science Quarterly,1984:518-539.
[87]Mathieson K. Predicting user intentions:comparing the technology acceptance model with the theory of planned behavior[J]. Information systems research,1991,2(3):173-191.
[88]Venkatesh V, Davis F D. A theoretical extension of the technology acceptance model:four longitudinal field studies[J]. Management science,2000,46(2):186-204.
[89]Lee S K, Cho Y H, Kim S H. Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations[J]. Information Sciences,2010,180(11): 2142-2155.
[90]HUANG, Zhihua, Songnian LUa, and Aixin ZHANG. Implicit Rating Schemes Based on Observable Behaviors in P2P Trust Systems[J].2012.
[91]DeCoste D. Collaborative prediction using ensembles of maximum margin matrix factorizations[C]. Proceedings of the 23rd international conference on Machine learning. ACM,2006:249-256.
[2]Smith N C, Cooper-Martin E. Ethics and target marketing:The role of product harm and consumer vulnerability[J]. The Journal of Marketing,1997:1-20.
[3]Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J]. Knowledge and Data Engineering, IEEE Transactions on,2005,17(6):734-749.
[4]Salton, G. Automatic Text Processing. Addison-Wesley,1989.
[5]Statnikov, R. B. and J. B. Matusov. Multicriteria Optimization and Engineering. Chapman & Hall,1995.
[6]Billsus, D. and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction,10(2-3):147-180,2000.
[7]Marlin, B. Modeling User Rating Profiles for Collaborative Filtering. In Proceedings of the 17th Annual Conference on Neural Information Processing Systems (NIPS'03),2003.
[8]Ma H, Yang H, Lyu M R, et al. Sorec:social recommendation using probabilistic matrix factorization[C]. Proceedings of the 17th ACM conference on Information and knowledge management. ACM,2008:931-940.
[9]Walter F E, Battiston S, Schweitzer F. A model of a trust-based recommendation system on a social network[J]. Autonomous Agents and Multi-Agent Systems,2008,16(1):57-74.
[10]Debnath S, Ganguly N, Mitra P. Feature weighting in content based recommendation system using social network analysis[C]. Proceedings of the 17th international conference on World Wide Web. ACM,2008:1041-1042.
[11]Xiao B, Benbasat 1. E-commerce product recommendation agents:use, characteristics, and impact[J]. Mis Quarterly,2007,31(1):137-209.
[12]Swaminathan V. The impact of recommendation agents on consumer evaluation and choice: the moderating role of category risk, product complexity, and consumer knowledge[J]. Journal of Consumer Psychology,2003,13(1):93-101.
[13]Kramer T. The effect of measurement task transparency on preference construction and evaluations of personalized recommendations[J]. Journal of Marketing Research,2007:224-233.
[14]Yang F, WANG Z. A mobile location-based information recommendation system based on GPS and WEB2.0 services[J]. database,2009,7:8.
[15]Tung H W, Soo V W. A personalized restaurant recommender agent for mobile e-service[C] e-Technology, e-Commerce and e-Service,2004. EEE'04.2004 IEEE International Conference on. IEEE,2004:259-262.
[16]Yang W S, Cheng H C, Dia J B. A location-aware recommender system for mobile shopping environments[J]. Expert Systems with Applications,2008,34(1):437-445.
[17]Wasserman S, Faust K. Social network analysis:Methods and applications[M]. Cambridge university press,1994.
[18]Wellman B. For a social network analysis of computer networks:a sociological perspective on collaborative work and virtual community[C]. Proceedings of the 1996 ACM SIGCPR/SIGMIS conference on Computer personnel research. ACM,1996:1-11.
[19]Witt R E, Bruce G D. Group influence and brand choice congruence[J]. Journal of Marketing Research,1972,9(4):440-443.
[20]Bearden W O, Etzel M J. Reference group influence on product and brand purchase decisions[J]. Journal of Consumer research,1982:183-194.
[21]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[22]Massa P, Avesani P. Trust-aware recommender systems[C]. Proceedings of the 2007 ACM conference on Recommender systems. ACM,2007:17-24.
[23]Massa P, Bhattacharjee B. Using trust in recommender systems:an experimental analysis[M].Trust Management. Springer Berlin Heidelberg,2004:221-235.
[24]Golbeck J. Generating predictive movie recommendations from trust in social nerworks[M]. Springer Berlin Heidelberg,2006.
[25]Carmagnola F, Vemero F, Grillo P. Sonars:A social networks-based algorithm for social recommender systems[M]. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg,2009:223-234.
[26]Jamali M, Haffari G, Ester M. Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns[C]. Proceedings of the 20th international conference on World wide web. ACM,2011:527-536.
[27]Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms[C]. Proceedings of the 10th international conference on World Wide Web. ACM,2001: 285-295.
[28]Ma H, Zhou D, Liu C, et al. Recommender systems with social regularization[C]. Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011:287-296.
[29]Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer,2009,42(8):30-37.
[30]Kordumova S, Kostadinovska I, Barbieri M, et al. Personalized implicit learning in a music recommender system[M]. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg,2010:351-362.
[31]Nunez-Valdez E R, Cueva Lovelle J M, Sanjuan Martinez O, et al. Implicit feedback techniques on recommender systems applied to electronic books[J]. Computers in Human Behavior,2012.
[32]Kim J W, Lee B H, Shaw M J, et al. Application of decision-tree induction techniques to personalized advertisements on internet storefronts[J]. International Journal of Electronic Commerce,2001,5:45-62.
[33]Billsus D, Pazzani M J, Chen J. A learning agent for wireless news access[C]. Proceedings of the 5th international conference on Intelligent user interfaces. ACM,2000:33-36.
[34]Pazzani M, Billsus D. Learning and revising user profiles:The identification of interesting web sites[J]. Machine learning,1997,27(3):313-331.
[35]Pazzani M J, Billsus D. Content-based recommendation systems[M].The adaptive web. Springer Berlin Heidelberg,2007:325-341.
[36]Maltz D, Ehrlich K. Pointing the way:active collaborative filtering[C]. Proceedings of the SIGCHI conference on Human factors in computing systems. ACM Press/Addison-Wesley Publishing Co.,1995:202-209.
[37]Linden G, Smith B, York J. Amazon, com recommendations:Item-to-item collaborative filtering[J]. Internet Computing, IEEE,2003,7(1):76-80.
[38]Konstan J A, Miller B N, Maltz D, et al. GroupLens:applying collaborative filtering to Usenet news[J]. Communications of the ACM,1997,40(3):77-87.
[39]Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.,1998:43-52.
[40]Yoshii K, Goto M, Komatani K, et al. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model[J]. Audio, Speech, and Language Processing, IEEE Transactions on,2008,16(2):435-447.
[41]Li Q, Kim B M. An approach for combining content-based and collaborative filters[C]. Proceedings of the sixth international workshop on Information retrieval with Asian languages-Volume 11. Association for Computational Linguistics,2003:17-24.
[42]Popescul A, Pennock D M, Lawrence S. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments[C]. Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.,2001: 437-444.
[43]Mobasher B, Cooley R, Srivastava J. Automatic personalization based on Web usage mining[J]. Communications of the ACM,2000,43(8):142-151.
[44]Cho Y H, Kim J K. Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce[J]. Expert systems with Applications,2004,26(2):233-246.
[45]Mobasher B, Cooley R, Srivastava J. Automatic personalization based on Web usage mining[J]. Communications of the ACM,2000,43(8):142-151.
[46]Middleton S E, Shadbolt N R, De Roure D C. Ontological user profiling in recommender systems[J]. ACM Transactions on Information Systems (TOIS),2004,22(1):54-88.
[47]Zhang Y, Koren J. Efficient bayesian hierarchical user modeling for recommendation system[C]. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2007:47-54.
[48]Salton G. Automatic text processing[J]. Science,1970,168(3929):335-343.
[49]Phelan O, McCarthy K, Smyth B. Using twitter to recommend real-time topical news[C]. Proceedings of the third ACM conference on Recommender systems. ACM,2009:385-388.
[50]Niwa S, Honiden S. Web page recommender system based on folksonomy mining for itng'06 submissions[C]. Information Technology: New Generations,2006. ITNG 2006. Third International Conference on. IEEE,2006:388-393.
[51]Costa A, Guizzardi R, Guizzardi Filho G. J.:COReS:Context-aware, Ontology-based Recommender system for Service recommendation[C]. Proceedings of the 19th international conference on advanced information systems engineering (CAiSE'07).
[52]Park J H, Park W I, Kim Y K, et al. Personalized recommender system for resource sharing based on context-aware in ubiquitous environments[C]. Information Technology,2008. ITSim 2008. International Symposium on. IEEE,2008,4:1-5.
[53]Jain A K, Murty M N, Flynn P J. Data clustering: a review[J]. ACM computing surveys (CSUR),1999,31(3):264-323.
[54]George T, Merugu S. A scalable collaborative filtering framework based on co-clustering[C]. Data Mining, Fifth IEEE International Conference on. IEEE,2005:4 pp.
[55]Blanco-Femandez Y, Pazos-Arias J J, Gil-Solla A, et al. A multi-agent open architecture for a TV recommender system:a case study using a Bayesian strategy[C]. Multimedia Software Engineering,2004. Proceedings. IEEE Sixth International Symposium on. IEEE,2004:178-185.
[56]Quinlan J R. Induction of decision trees[J]. Machine learning,1986,1(1):81-106.
[57]Cho Y H, Kim J K, Kim S H. A personalized recommender system based on web usage mining and decision tree induction[J]. Expert Systems with Applications,2002,23(3):329-342.
[58]Golbandi N, Koren Y, Lempel R. Adaptive bootstrapping of recommender systems using decision trees[C]. Proceedings of the fourth ACM international conference on Web search and data mining. ACM,2011:595-604.
[59]Dayhoff J E, DeLeo J M. Artificial neural networks[J]. Cancer,2001,91(S8):1615-1635.
[60]Christakou C, Vrettos S, Stafylopatis A. A hybrid movie recommender system based on neural networks[J]. International Journal on Artificial Intelligence Tools,2007,16(05):771-792.
[61]Kurkovsky S, Harihar K. Using ubiquitous computing in interactive mobile marketing[J]. Personal and Ubiquitous Computing,2006,10(4):227-240.
[62]Kurkovsky S, Harihar K. Using ubiquitous computing in interactive mobile marketing[J]. Personal and Ubiquitous Computing,2006,10(4):227-240.
[63]De Pessemier T, Deryckere T, Martens L. Extending the Bayesian classifier to a context-aware recommender system for mobile devices[C]. Internet and Web Applications and Services (ICIW),2010 Fifth International Conference on. IEEE,2010:242-247.
[64]Fano A E. Shopper's eye:using location-based filtering for a shopping agent in the physical world[C]. Proceedings of the second international conference on Autonomous agents. ACM,1998: 416-421.
[65]Kawashima H, Matsushita T, Satake S, et al. PORSCHE:A physical objects recommender system for cell phone users[C]. Proceedings of the Second International Workshop on Personalized Context Modeling and Management for UbiComp Applications, Orange County, California.2006.
[66]Bohnert F. Adaptive user modelling and recommendation in constrained physical environments[C]. Adaptive Hypermedia and Adaptive Web-Based Systems. Springer Berlin Heidelberg,2008:378-383.
[67]Massa P, Avesani P. Trust-aware collaborative filtering for recommender systems[M]. On the Move to Meaningful Internet Systems 2004:CoopIS, DOA, and ODBASE. Springer Berlin Heidelberg,2004:492-508.
[68]Moghaddam S, Jamali M, Ester M, et al. FeedbackTrust:using feedback effects in trust-based recommendation systems[C]. Proceedings of the third ACM conference on Recommender systems. ACM,2009:269-272.
[69]Liu H, Maes P. Interestmap:Harvesting social network profiles for recommendations[J]. Beyond Personalization-IUI,2005.
[70]Crandall D, Cosley D, Huttenlocher D, et al. Feedback effects between similarity and social influence in online communities[C]. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2008:160-168.
[71]Bonhard P, Sasse M A.'Knowing me, knowing you'—sing profiles and social networking to improve recommender systems[J]. BT Technology Journal,2006,24(3):84-98.
[72]Yang W S, Dia J B. Discovering cohesive subgroups from social networks for targeted advertising[J]. Expert Systems with Applications,2008,34(3):2029-2038.
[73]Mudambi S M, Schuff D. What makes a helpful online review? A study of customer reviews on Amazon. com[J]. Mis Quarterly,2010,34(1):185-200.
[74]Awad N F, Ragowsky A. Establishing trust in electronic commerce through online word of mouth:an examination across genders[J]. Journal of Management Information Systems,2008, 24(4):101-121.
[75]Jansen B J, Zhang M, Sobel K, et al. Twitter power:Tweets as electronic word of mouth[J]. Journal of the American society for information science and technology,2009,60(11):2169-2188.
[76]Peres R, Van den Bulte C. How customer word of mouth affects the benefits of new product exclusivity to distributors[R]. Working Paper,2010.
[77]Zhu F, Zhang X. Impact of online consumer reviews on sales:The moderating role of product and consumer characteristics[J]. Journal of Marketing,2010,74(2):133-148.
[78]Vermeulen I E, Seegers D. Tried and tested:The impact of online hotel reviews on consumer consideration[J]. Tourism Management,2009,30(1):123-127.
[79]Lee T Q, Park Y, Park Y T. A time-based approach to effective recommender systems using implicit feedback[J]. Expert systems with applications,2008,34(4):3055-3062.
[80]Oard D W, Kim J. Implicit feedback for recommender systems[C]. Proceedings of the AAAI workshop on recommender systems. Wollongong,1998:81-83.
[81]Kelly D, Belkin N J. Reading time, scrolling and interaction:exploring implicit sources of user preferences for relevance feedback[C]. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2001:408-409.
[82]Jawaheer G, Szomszor M, Kostkova P. Comparison of implicit and explicit feedback from an online music recommendation service[C]. Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems. ACM,2010:47-51.
[83]Shahabi C, Chen Y S. An adaptive recommendation system without explicit acquisition of user relevance feedback[J]. Distributed and Parallel Databases,2003,14(2):173-192.
[84]Kim J, Oard D W, Romanik K. Using implicit feedback for user modeling in internet and intranet searching[J].2000.
[85]Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets[C].Data Mining,2008. ICDM'08. Eighth IEEE International Conference on IEEE,2008:263-272.
[86]Recker M M, Walker A. Supporting "Word-of-Mouth" social networks through collaborative information filtering[J]. Journal of Interactive Learning Research,2003,14(1):79-98.
[87]Webster A, Vassileva J. Push-Poll recommender system:supporting word of mouth[M]. User Modeling 2007. Springer Berlin Heidelberg,2007:278-287.
[88]Li F, Du T C. Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs[J]. Decision Support Systems,2011,51(1): 190-197.
[89]Heath T, Motta E, Petre M. Person to person trust factors in word of mouth recommendation[J].2006.
[90]Heath T, Motta E, Petre M. Computing word-of-mouth trust relationships in social networks from semantic web and web 2.0 data sources[J].2007.
[91]Kim Y, Srivastava J. Impact of social influence in e-commerce decision making[C]. Proceedings of the ninth international conference on Electronic commerce. ACM,2007:293-302.
[92]Arazy O, Kumar N, Shapira B. A theory-driven design framework for social recommender systems[J]. Journal of the Association for Information Systems,2010,11(9):455-490.
[93]Raven B H. Social influence and power[R]. CALIFORNIA UNIV LOS ANGELES,1964.
[94]Turner J C. Social influence[M]. Milton Keynes:Open University Press,1991.
[95]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[96]Festinger L. A theory of social comparison processes[J]. Human relations,1954,7(2): 117-140.
[97]Zajonc R B. Social fecilitation[M]. Research Center for Group Dynamics, Institute for Social Research, University of Michigan,1965.
[98]Bumkrant R E, Cousineau A. Informational and normative social influence in buyer behavior[J]. Journal of Consumer Research,1975:206-215.
[99]Festinger L. Informal social communication[J]. Psychological review,1950,57(5):271.
[100]Mullen B, Copper C. The relation between group cohesiveness and performance:An integration[R]. SYRACUSE UNIV NY,1995.
[101]Witt R E. Informal social group influence on consumer brand choice[J]. Journal of Marketing Research,1969:473-476.
[102]Moschis G P. Social comparison and informal group influence[J]. Journal of Marketing Research,1976:237-244.
[103]Stafford J E. Effects of group influences on consumer brand preferences[J]. Journal of marketing Research,1966:68-75.
[104]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[105]Kelman H C. Processes of opinion change[J]. The Planning of Change,1969:470-496.
[106]Park C W, Lessig V P. Students and housewives:Differences in susceptibility to reference group influence[J]. Journal of Consumer Research,1977:102-110.
[107]Kaplan M F, Miller C E. Group decision making and normative versus informational influence:Effects of type of issue and assigned decision rule[J]. Journal of Personality and Social Psychology,1987,53(2):306.
[108]Mangleburg T F, Doney P M, Bristol T. Shopping with friends and teens' susceptibility to peer influence[J]. Journal of Retailing,2004,80(2):101-116.
[109]Kelman H C. Processes of opinion change[J]. Public opinion quarterly,1961,25(1):57-78.
[110]Leskovec J, Lang K J, Dasgupta A, et al. Community structure in large networks:Natural cluster sizes and the absence of large well-defined clusters[J]. Internet Mathematics,2009,6(1): 29-123.
[111]Brown J J, Reingen P H. Social ties and word-of-mouth referral behavior[J], Journal of Consumer Research,1987:350-362.
[112]McPherson J M, Smith-Lovin L. Homophily in voluntary organizations:Status distance and the composition of face-to-face groups[J]. American sociological review,1987:370-379.
[113]Granovetter M S. The strength of weak ties[J]. American journal of sociology,1973: 1360-1380.
[114]Brown J, Broderick A J, Lee N. Word of mouth communication within online communities: Conceptualizing the online social network[J]. Journal of interactive marketing,2007,21(3):2-20.
[115]Travers J, Milgram S. An experimental study of the small world problem [J]. Sociometry, 1969:425-443.
[116]Onnela J P, Saramaki J, Hyvonen J, et al. Structure and tie strengths in mobile communication networks[J]. Proceedings of the National Academy of Sciences,2007,104(18): 7332-7336.
[117]Granovetter M. The strength of weak ties: A network theory revisited[J]. Sociological theory, 1983,1(1):201-233.
[118]Holes S. The Social Structure of Competition[J].1992.
[119]Scott J. Social Network Analysis:A Handbook[M]. SAGE Publications Limited,2000.
[120]Haythomthwaite C. Social network analysis:An approach and technique for the study of information exchange[J]. Library & Information Science Research,1996,18(4):323-342.
[121]Morrison E W. NEWCOMERS' RELATIONSHIPS:THE ROLE OF SOCIAL NETWORK TIES DURING SOCIALIZATION[J]. Academy of management Journal,2002,45(6):1149-1160.
[122]Lewis K, Kaufman J, Gonzalez M, et al. Tastes, ties, and time:A new social network dataset using Facebook. com[J]. Social Networks,2008,30(4):330-342.
[123]Laumann E O. Bonds of pluralism:The form and substance of urban social networks[M]. J. Wiley,1973.
[124]Campbell K E, Marsden P V, Hurlbert J S. Social resources and socioeconomic status[J]. Social networks,1986,8(1):97-117.
[125]Marsden P V. Core discussion networks of Americans[J]. American sociological review, 1987:122-131.
[126]Daly E M, Haahr M. Social network analysis for routing in disconnected delay-tolerant manets[C]. Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing. ACM,2007:32-40.
[127]Friedkin N E. Structural bases of interpersonal influence in groups:A longitudinal case study[J]. American Sociological Review,1993:861-872.
[128]Ibarra H, Andrews S B. Power, social influence, and sense making:Effects of network centrality and proximity on employee perceptions[J]. Administrative science quarterly,1993: 277-303.
[129]Sparrowe R T, Liden R C, Wayne S J, et al. Social Networks and the Performance of Individuals and Groups[J]. Academy of management journal,2001,44(2):316-325.
[130]Cook K S, Emerson R M. Power, equity and commitment in exchange networks[J]. American Sociological Review,1978:721-739.
[131]Brass D J. Being in the right place:A structural analysis of individual influence in an organization[J]. Administrative Science Quarterly,1984:518-539.
[132]Mathieson K. Predicting user intentions:comparing the technology acceptance model with the theory of planned behavior[J]. Information systems research,1991,2(3):173-191.
[133]Venkatesh V, Davis F D. A theoretical extension of the technology acceptance model:four longitudinal field studies[J]. Management science,2000,46(2):186-204.
[134]Lee S K, Cho Y H, Kim S H. Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations[J]. Information Sciences,2010,180(11):2142-2155.
[135]HUANG, Zhihua, Songnian LUa, and Aixin ZHANG. Implicit Rating Schemes Based on Observable Behaviors in P2P Trust Systems[J].2012.
[136]DeCoste D. Collaborative prediction using ensembles of maximum margin matrix factorizations[C]. Proceedings of the 23rd international conference on Machine learning. ACM, 2006:249-256.
[137]Miyahara K, Pazzani M J. Collaborative filtering with the simple Bayesian classifier[M]. PRICAI 2000 Topics in Artificial Intelligence. Springer Berlin Heidelberg,2000:679-689.
[138]Carterette B, Bennett P N. Evaluation measures for preference judgments[C]. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2008:685-686.
[139]Ellison N B. Social network sites:Definition, history, and scholarship[J]. Journal of Computer-Mediated Communication,2007,13(1):210-230.
[1]Rossi P E, McCulloch R E, Allenby G M. The value of purchase history data in target marketing[J]. Marketing Science,1996,15(4):321-340.
[2]Smith N C, Cooper-Martin E. Ethics and target marketing:The role of product harm and consumer vulnerability [J]. The Journal of Marketing,1997:1-20.
[3]Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J]. Knowledge and Data Engineering, IEEE Transactions on,2005,17(6):734-749.
[4]Salton, G. Automatic Text Processing. Addison-Wesley,1989.
[5]Stamikov, R B. and J. B. Matusov. Multicriteria Optimization and Engineering. Chapman & Hall,1995.
[6]Billsus, D. and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction,10(2-3):147-180,2000.
[7]Marlin, B. Modeling User Rating Profiles for Collaborative Filtering. In Proceedings of the 17th Annual Conference on Neural Information Processing Systems (NIPS'03),2003.
[8]Ma H, Yang H, Lyu M R, et al. Sorec:social recommendation using probabilistic matrix factorization[C]. Proceedings of the 17th ACM conference on Information and knowledge management. ACM,2008:931-940.
[9]Walter F E, Battiston S, Schweitzer F. A model of a trust-based recommendation system on a social network[J]. Autonomous Agents and Multi-Agent Systems,2008,16(1):57-74.
[10]Debnath S, Ganguly N, Mitra P. Feature weighting in content based recommendation system using social network analysis[C]. Proceedings of the 17th international conference on World Wide Web. ACM,2008:1041-1042.
[11]Xiao B, Benbasat I. E-commerce product recommendation agents:use, characteristics, and impact[J]. Mis Quarterly,2007,31(1):137-209.
[12]Swaminathan V. The impact of recommendation agents on consumer evaluation and choice: the moderating role of category risk, product complexity, and consumer knowledge[J]. Journal of Consumer Psychology,2003,13(1):93-101.
[13]Kramer T. The effect of measurement task transparency on preference construction and evaluations of personalized recommendations[J]. Journal of Marketing Research,2007: 224-233.
[14]Yang F, WANG Z. A mobile location-based information recommendation system based on GPS and WEB2.0 services[J]. database,2009,7:8.
[15]Tung H W, Soo V W. A personalized restaurant recommender agent for mobile e-service[C] e-Technology, e-Commerce and e-Service,2004. EEE'04.2004 IEEE International Conference on. IEEE,2004:259-262.
[16]Yang W S, Cheng H C, Dia J B. A location-aware recommender system for mobile shopping environments[J]. Expert Systems with Applications,2008,34(1):437-445.
[17]Wasserman S, Faust K. Social network analysis:Methods and applications[M]. Cambridge university press,1994.
[18]Wellman B. For a social network analysis of computer networks:a sociological perspective on collaborative work and virtual community[C]. Proceedings of the 1996 ACM SIGCPR/SIGMIS conference on Computer personnel research. ACM,1996:1-11.
[19]Massa P, Avesani P. Trust-aware recommender systems[C]. Proceedings of the 2007 ACM conference on Recommender systems. ACM,2007:17-24.
[20]Massa P, Bhattacharjee B. Using trust in recommender systems:an experimental analysis[M].Trust Management. Springer Berlin Heidelberg,2004:221-235.
[21]Golbeck J. Generating predictive movie recommendations from trust in social networks[M]. Springer Berlin Heidelberg,2006.
[22]Carmagnola F, Vernero F, Grillo P. Sonars:A social networks-based algorithm for social recommender systems[M]. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg,2009:223-234.
[23]Jamah" M, Haffari G, Ester M. Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating pattems[C]. Proceedings of the 20th international conference on World wide web. ACM,2011:527-536.
[24]Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms[C]. Proceedings of the 10th international conference on World Wide Web. ACM, 2001:285-295.
[25]Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer,2009,42(8):30-37.
[26]Kordumova S, Kostadinovska I, Barbieri M, et al. Personalized implicit learning in a music recommender system[M]. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg,2010:351-362.
[27]Nunez-Valdez E R, Cueva Lovelle J M, Sanjuan Martinez O, et al. Implicit feedback techniques on recommender systems applied to electronic books[J]. Computers in Human Behavior,2012.
[28]Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.,1998:43-52.
[29]Salton G. Automatic text processing[J]. Science,1970,168(3929):335-343.
[30]Phelan O, McCarthy K, Smyth B. Using twitter to recommend real-time topical news[C]. Proceedings of the third ACM conference on Recommender systems. ACM,2009:385-388.
[31]Niwa S, Honiden S. Web page recommender system based on folksonomy mining for itng'06 submissions[C]. Information Technology:New Generations,2006. ITNG 2006. Third International Conference on. IEEE,2006:388-393.
[32]Jain A K, Murty M N, Flynn P J. Data clustering:a review[J]. ACM computing surveys (CSUR),1999,31(3):264-323.
[33]George T, Merugu S. A scalable collaborative filtering framework based on co-clustering[C]. Data Mining, Fifth IEEE International Conference on. IEEE,2005:4 pp.
[34]Blanco-Fernandez Y, Pazos-Arias J J, Gil-Solla A, et al. A multi-agent open architecture for a TV recommender system:a case study using a Bayesian strategy[C]. Multimedia Software Engineering,2004. Proceedings. IEEE Sixth International Symposium on. IEEE,2004: 178-185.
[35]Quinlan J R. Induction of decision trees[J]. Machine learning,1986,1(1):81-106.
[36]Cho Y H, Kim J K, Kim S H. A personalized recommender system based on web usage mining and decision tree induction[J]. Expert Systems with Applications,2002.23(3): 329-342.
[37]Golbandi N, Koren Y, Lempel R. Adaptive bootstrapping of recommender systems using decision trees[C]. Proceedings of the fourth ACM international conference on Web search and data mining. ACM,2011:595-604.
[38]Dayhoff J E, DeLeo J M. Artificial neural networks[J]. Cancer,2001,91(S8):1615-1635.
[39]Christakou C, Vrettos S, Stafylopatis A. A hybrid movie recommender system based on neural networks[J]. International Journal on Artificial Intelligence Tools,2007,16(05): 771-792.
[40]Massa P, Avesani P. Trust-aware collaborative filtering for recommender systems[M]. On the Move to Meaningful Internet Systems 2004:CoopIS, DOA, and ODBASE. Springer Berlin Heidelberg,2004:492-508.
[41]Crandall D, Cosley D, Huttenlocher D, et al. Feedback effects between similarity and social influence in online communities[C]. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2008:160-168.
[42]Mudambi S M, Schuff D. What makes a helpful online review? A study of customer reviews on Amazon. com[J]. Mis Quarterly,2010,34(1):185-200.
[43]Awad N F, Ragowsky A. Establishing trust in electronic commerce through online word of mouth:an examination across genders[J]. Journal of Management Information Systems,2008, 24(4):101-121.
[44]Jansen B J, Zhang M, Sobel K, et al. Twitter power:Tweets as electronic word of mouth[J]. Journal of the American society for information science and technology,2009, 60(11):2169-2188.
[45]Peres R, Van den Bulte C. How customer word of mouth affects the benefits of new product exclusivity to distributors[R]. Working Paper,2010.
[46]Zhu F, Zhang X. Impact of online consumer reviews on sales:The moderating role of product and consumer characteristics[J]. Journal of Marketing,2010,74(2):133-148.
[47]Vermeulen I E, Seegers D. Tried and tested:The impact of online hotel reviews on consumer consideration[J]. Tourism Management,2009,30(1):123-127.
[48]Lee T Q, Park Y, Park Y T. A time-based approach to effective recommender systems using implicit feedback[J]. Expert systems with applications,2008,34(4):3055-3062.
[49]Oard D W, Kim J. Implicit feedback for recommender systems[C]. Proceedings of the AAAI workshop on recommender systems. Wollongong,1998:81-83.
[50]Kelly D, Belkin N J. Reading time, scrolling and interaction:exploring implicit sources of user preferences for relevance feedback[C]. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2001: 408-409.
[51]Jawaheer G, Szomszor M, Kostkova P. Comparison of implicit and explicit feedback from an online music recommendation service[C]. Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems. ACM,2010:47-51.
[52]Shahabi C, Chen Y S. An adaptive recommendation system without explicit acquisition of user relevance feedback[J]. Distributed and Parallel Databases,2003,14(2):173-192.
[53]Kim J, Oard D W, Romanik K. Using implicit feedback for user modeling in internet and intranet searching[J].2000.
[54]Recker M M, Walker A. Supporting "Word-of-Mouth" social networks through collaborative information filtering[J]. Journal of Interactive Learning Research,2003,14(1): 79-98.
[55]Webster A, Vassileva J. Push-Poll recommender system:supporting word of mouth[M]. User Modeling 2007. Springer Berlin Heidelberg,2007:278-287.
[56]Li F. Du T C. Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs[J]. Decision Support Systems,2011, 51(1):190-197.
[57]Heath T, Motta E, Petre M. Person to person trust factors in word of mouth recommendation[J].2006.
[58]Heath T, Motta E, Petre M. Computing word-of-mouth trust relationships in social networks from semantic web and web 2.0 data sources[J].2007.
[59]Kim Y, Srivastava J. Impact of social influence in e-commerce decision making[C]. Proceedings of the ninth international conference on Electronic commerce. ACM,2007: 293-302.
[60]Arazy O, Kumar N, Shapira B. A theory-driven design framework for social recommender systems[J]. Journal of the Association for Information Systems,2010.11(9):455-490.
[61]Turner J C. Social influence[M]. Milton Keynes:Open University Press,1991.
[62]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[63]Festinger L. A theory of social comparison processes[J]. Human relations,1954,7(2): 117-140.
[64]Zajonc R B. Social facilitation[M]. Research Center for Group Dynamics, Institute for Social Research, University of Michigan,1965.
[65]Deutsch M, Gerard H B. A study of normative and informational social influences upon individual judgment[J]. Journal of abnormal and social psychology,1955,51(3):629-636.
[66]Kelman H C. Processes of opinion change[J]. The Planning of Change,1969:470-496.
[67]Park C W, Lessig V P. Students and housewives:Differences in susceptibility to reference group influence[J]. Journal of Consumer Research,1977:102-110.
[68]Leskovec J, Lang K J, Dasgupta A, et al. Community structure in large networks:Natural cluster sizes and the absence of large well-defined clusters[J]. Internet Mathematics,2009, 6(1):29-123.
[69]Brown J J, Reingen P H. Social ties and word-of-mouth referral behavior[J]. Journal of Consumer Research,1987:350-362.
[70]McPherson J M, Smith-Lovin L. Homophily in voluntary organizations:Status distance and the composition of face-to-face groups[J]. American sociological review,1987:370-379.
[71]Granovetter M S. The strength of weak ties[J]. American journal of sociology,1973: 1360-1380.
[72]Brown J, Broderick A J, Lee N. Word of mouth communication within online communities: Conceptualizing the online social network[J]. Journal of interactive marketing,2007.21(3): 2-20.
[73]Holes S. The Social Structure of Competition[J].1992.
[74]Scott J. Social Network Analysis:A Handbook[M]. SAGE Publications Limited,2000.
[75]Haythornthwaite C. Social network analysis:An approach and technique for the study of information exchange[J]. Library & Information Science Research,1996,18(4):323-342.
[76]Morrison E W. NEWCOMERS'RELATIONSHIPS:THE ROLE OF SOCIAL NETWORK TIES DURING SOCIALIZATION[J]. Academy of management Journal,2002,45(6): 1149-1160.
[77]Lewis K, Kaufman J, Gonzalez M, et al. Tastes, ties, and time:A new social network dataset using Facebook. com[J]. Social Networks,2008,30(4):330-342.
[78]Laumann E O. Bonds of pluralism:The form and substance of urban social networks[M]. J. Wiley,1973.
[79]Campbell K E, Marsden P V, Hurlbert J S. Social resources and socioeconomic status[J]. Social networks,1986,8(1):97-117.
[80]Marsden P V. Core discussion networks of Americans[J]. American sociological review, 1987:122-131.
[81]Daly E M, Haahr M. Social network analysis for routing in disconnected delay-tolerant manets[C]. Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing. ACM,2007:32-40.
[82]Friedkin N E. Structural bases of interpersonal influence in groups: A longitudinal case study[J]. American Sociological Review,1993:861-872.
[83]Ibarra H, Andrews S B. Power, social influence, and sense making:Effects of network centrality and proximity on employee perceptions[J]. Administrative science quarterly,1993: 277-303.
[84]Sparrowe R T, Liden R C, Wayne S J, et al. Social Networks and the Performance of Individuals and Groups[J]. Academy of management journal,2001,44(2):316-325.
[85]Cook K S, Emerson R M. Power, equity and commitment in exchange networks[J]. American Sociological Review,1978:721-739.
[86]Brass D J. Being in the right place:A structural analysis of individual influence in an organization[J]. Administrative Science Quarterly,1984:518-539.
[87]Mathieson K. Predicting user intentions:comparing the technology acceptance model with the theory of planned behavior[J]. Information systems research,1991,2(3):173-191.
[88]Venkatesh V, Davis F D. A theoretical extension of the technology acceptance model:four longitudinal field studies[J]. Management science,2000,46(2):186-204.
[89]Lee S K, Cho Y H, Kim S H. Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations[J]. Information Sciences,2010,180(11): 2142-2155.
[90]HUANG, Zhihua, Songnian LUa, and Aixin ZHANG. Implicit Rating Schemes Based on Observable Behaviors in P2P Trust Systems[J].2012.
[91]DeCoste D. Collaborative prediction using ensembles of maximum margin matrix factorizations[C]. Proceedings of the 23rd international conference on Machine learning. ACM,2006:249-256.
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