融合贝叶斯推理与随机游走的好友推荐
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摘要
随机游走是一种应对推荐应用中用户规模庞大、数据稀疏等问题的有效方法.鉴于社交网络用户间亲密度差异、反向社交影响力等因素对基于随机游走的推荐具有积极影响,提出了一种引入频繁项挖掘来计算用户社交亲密度,进而优化转移概率矩阵,并与局部反向游走相结合的随机游走改进模型.此外,为了有效利用用户属性信息,提出了一种用户潜在好友关系推断的贝叶斯推理模型,并与随机游走改进模型协同应用,进一步提升了好友推荐性能.真实数据集上的对比实验验证了提出算法的有效性.
Random walk is an effective strategy for dealing with a large user base as well as data sparsity in recommendation problems.However,the current work on recommendation problems do not take full account of the impact implied by both the intimacy difference between users and the reverse social influence.This paper presents an optimized friend recommendation model based on random walk,which introduces frequent pattern mining to capture user intimacy and to optimize the transition probability matrix,and is combined with local reverse search to implement recommendations.In order to make full use of users' attribute information,a Bayesian inference model is proposed for analyzing users' potential friend relationships and combined with random walk to provide better recommendation services.Experiments on real datasets demonstrated the effectiveness of the proposed method.
Random walk is an effective strategy for dealing with a large user base as well as data sparsity in recommendation problems.However,the current work on recommendation problems do not take full account of the impact implied by both the intimacy difference between users and the reverse social influence.This paper presents an optimized friend recommendation model based on random walk,which introduces frequent pattern mining to capture user intimacy and to optimize the transition probability matrix,and is combined with local reverse search to implement recommendations.In order to make full use of users' attribute information,a Bayesian inference model is proposed for analyzing users' potential friend relationships and combined with random walk to provide better recommendation services.Experiments on real datasets demonstrated the effectiveness of the proposed method.
引文
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[21]DBLP:Computer science bibliography[DB/OL].[2017-05-05].http://dblp.uni-trier.de/db/.
[2]HAN J W,PEI J,YIN Y W,et al.Mining Frequent Patterns without Candidate Generation:A Frequent-Pattern Tree Approach[J].Data Mining and Knowledge Discovery,2004,8(1):53-87.
[3]KOSKINEN J H,ROBINS G L,WANG P,et al.Bayesian analysis for partially observed network data,missing ties,attributes and actors[J].Social Networks,2013,35(4):514-527.
[4]BIAN L,HOLTZMAN H,HUYNH T,et al.MatchMaker:A friend recommendation system through TV character matching[C]//Proceedings of Consumer Communications and Networking Conference.IEEE,2012:714-718.
[5]WANG S S,LENG S P.Friend recommendation method for mobile social networks[J].Journal of Computer Applications,2016,36(9):2386-2389+2395.
[6]HUANG S,ZHANG J,WANG L,et al.Social friend recommendation based on multiple network correlation[J].IEEE Transactions on Multimedia,2016,18(2):287-299.
[7]CARMEL D,ZWERDLING N,GUY I,et al.Personalized social search based on the user's social network[C]//Proceedings of the 18th ACM Conference on Information and Knowledge Anagement,ACM,2009:1227-1236.
[8]HAVELIWALA T,KAMVAR S,JEH G.An analytical comparison of approaches to personalizing PageRank[R/OL].(2003-06-19)[2017-05-05].http://ilpubs.Stanford.edu:8090/596/.
[9]KONSTAS I,STATHOPOULOS V,JOSE J M.On Social Networks and Collaborative Recommendation[C]//Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2009:195-202.
[10]CHENG H,TAN P N,STICKLEN J,et al.Recommendation via query centered random walk on k-partite graph[C]//Proceedings of IEEE International Conference on Data Mining.IEEE,2007:457-462.
[11]FOUSS F,PIROTTE A,RENDERS J M,et al.Random walk computation of similarities between nodes of a graph with application to collaborative recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2007,19(3):355-369.
[12]JIANG M,CUI P,CHEN X,et al.Social recommendation with cross-domain transferable knowledge[J].IEEE Transactions on Knowledge&Data Engineering,2015,27(11):3084-3097.
[13]ZHANG L Y,XU J,LI C P.A random-walk based recommendation algorithm considering item categories[J].Neurocomputing,2013,120:391-396.
[14]于劲松,沈琳,唐荻音,等.基于贝叶斯网络的故障诊断系统性能评价[J].北京航空航天大学学报,2016,42(1):35-40.
[15]刘健,赵刚,郑运鹏.基于AHP-贝叶斯网络的信息安全风险态势分析模型[J].北京信息科技大学学报(自然科学版),2015(3):68-74.
[16]GATTI E,LUCIANI D,STELLA F.A continuous time Bayesian network model for cardiogenic heart failure[J].Flexible Services and Manufacturing Journal,2012,24(4):496-515.
[17]WATTS D J.六度分隔:一个相互连接的时代的科学[M].陈禹,等译.北京:中国人民大学出版社,2011.
[18]DOMINGOS P,PAZZANI M.On the optimality of the simple Bayesian classifier under zero-one loss[J].Machine Learning,1997,29(2-3):103-130.
[19]LIU Y,SIMEONE O,HAIMOVICH A M,et al.Modulation classification via gibbs sampling based on a latent dirichlet Bayesian network[J].IEEE Signal Processing Letters,2014,21(9):1135-1139.
[20]LESKOVEC J.Stanford large network dataset collection[DB/OL].[2017-05-05].http://snap.stanford.edu/data/index.html.
[21]DBLP:Computer science bibliography[DB/OL].[2017-05-05].http://dblp.uni-trier.de/db/.