群落标签推荐系统体系结构及关键问题研究
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摘要
随着信息通讯技术的发展，互联网的普及，如何帮助用户在海量的信息中找到其需要的信息成为亟待解决的问题。推荐系统作为克服信息过载，帮助用户发现所需信息的工具应运而生。推荐系统通过分析用户兴趣特征，物品属性特征，用户对物品的操作行为，建立用户兴趣模型，预测用户对物品的喜好，从而实施推荐。近年来推荐系统被广泛应用于电子商务等领域，在学术上也得到了广泛而深入的研究，但是随着数据规模的增大，推荐的内容和用户需求都朝着多元化的方向发展，推荐系统在体系架构，推荐结果的多样性和准确性，算法效率等方面都面临着一系列挑战。本文提出了群落标签推荐系统体系结构，应用融合标签的推荐模型和算法，解决推荐系统在预测准确率，推荐结果多样性，以及推荐算法效率三方面的问题，并在标准数据集上验证算法和模型的有效性。本文的主要工作和贡献如下：
1在群落标签推荐系统的模型设计方面：本文以提高推荐系统的综合质量为目标，在分析现有的推荐系统模型的基础上，提出了融合群落标签信息的推荐系统模型，改进推荐的准确性、多样性和效率。并在此基础上设计推荐系统的体系结构，根据用户的实际需求选择推荐算法和策略，提高系统的灵活性与适用性。
2在提升推荐内容多样性和分析用户对于不同领域物品兴趣标签的关联研究方面：本文分析了跨域推荐问题，提出了基于域间近邻模型的跨域推荐算法，并且通过物品的群落标签，建立用户对于不同领域物品的喜好在语义标签的关联关系，从而挖掘领域间喜好的关联规则。实验结果表明，域间近邻模型能够有效地解决跨域交叉推荐问题，即可以根据用户对一类物品的喜好向其推荐另一类的物品；同时采用关联规则的方法分析物品标签，挖掘用户对于不同类别物品的喜好在语义上的关联关系，为跨域推荐提供解释。
3在应用标签改进推荐准确性方面：分别在图模型和潜在因素模型中融合属性标签，提高Top-N推荐的命中率和预测评分推荐的准确度。实验结果表明，相对于经典的协同过滤推荐算法，融合属性标签信息的推荐模型能够提高推荐的准确性。
4在提高推荐算法的效率研究方面：针对现有协同过滤推荐算法的效率问题，分别在聚类模型和奇异值分解模型的基础上，提出了基于概率聚类的预测评分模型和增量式奇异值分解模型。实验验证，相比于经典的推荐算法，本文提出的高效预测评分算法在推荐效率、准确性等方面有较大的优势，可以用于实时推荐。
本文最后通过实现面向读物的Readings推荐服务，验证了群落标签推荐系统架构和相关的算法可以被应用于实际的推荐系统中，能够有效地改进读物推荐的准确性、多样性和效率，提高推荐系统的综合质量。
Along with the development of the information technology and communicationtechnology, and the popularization of internet, how to help people to find the usefulknowledge in vast quantities of information is a problem that is exigent to be solved.The personalized recommendation system is a useful tool to conquer the informationoverload, and helps the users to find the necessary information. The recommendationsystem builds the interesting model through analyzing the characteristics of users(items), and the users’ operating behaviors on items. Based on the models, therecommendation system can predict the users’ preferences to the items, andrecommend the appropriate items. In recent years, the recommendation system hasbeen widely used in industry, such as e-commerce, and researched deeply in theacademic community. However, along with the information requirements of usershave developed into diversification, there are some challenges in recommendationsystem, such as the architecture of recommendation system, the efficiency ofalgorithms, the diversity and accuracy of recommended items. This paper mainlystudies on the architecture of multitasking recommendation system, the models andalgorithms of recommendation based on the collaborative filtering, and verify theiravailability through the standard data sets. The main contributions of our researchare as following:
1. In terms of architecture of recommendation system: our paper designs theprototype of recommendation system, which has5main components, includinginteraction interface, data acquisition and preprocess, behavior analysis and featureextraction, multitasking recommendation module, and feedback analysis. Thepluggable recommendation architecture can choose and deploy the algorithm andmodel according to the users’ requirements. The recommender system can make useof the system architecture and efficient recommending algorithm to get the highaccuracy and diversified recommended items.
2. In terms of improving the diversity of recommended items: we propose the cross domain recommendation based on the cross nearest neighbor model. Throughanalyzing the Folksonomy of different kinds of recommended items, we can minethe association rules in semantics among users’ interests in different domains. Theexperimental results show that our method can implement the cross domainrecommendation and get the association rules among users’ preferences in differentdomains with tags.
3. In terms of improving the accuracy of recommended algorithm: we proposeto integrate the attributes into the graph based model and latent factor model in orderto improve the accuracy of Top-N recommend task and predictive rating task.
4. In terms of improving the efficiency of recommended algorithm: we proposetwo models: one is the probabilistic clustering model based on clustering algorithm,the other is the incremental SVD model based on the singular value decomposealgorithm. The two models can be used to real-time recommendation.
Based on the architecture and algorithms which are introduced in this paper, webuilt the book recommendation system: Readings. Our research satisfies algorithminnovation and engineering practices. Our multitasking recommending architectureand recommending algorithms can improve the effectiveness, accuracy and diversityof recommendation to a large extent.
1在群落标签推荐系统的模型设计方面：本文以提高推荐系统的综合质量为目标，在分析现有的推荐系统模型的基础上，提出了融合群落标签信息的推荐系统模型，改进推荐的准确性、多样性和效率。并在此基础上设计推荐系统的体系结构，根据用户的实际需求选择推荐算法和策略，提高系统的灵活性与适用性。
2在提升推荐内容多样性和分析用户对于不同领域物品兴趣标签的关联研究方面：本文分析了跨域推荐问题，提出了基于域间近邻模型的跨域推荐算法，并且通过物品的群落标签，建立用户对于不同领域物品的喜好在语义标签的关联关系，从而挖掘领域间喜好的关联规则。实验结果表明，域间近邻模型能够有效地解决跨域交叉推荐问题，即可以根据用户对一类物品的喜好向其推荐另一类的物品；同时采用关联规则的方法分析物品标签，挖掘用户对于不同类别物品的喜好在语义上的关联关系，为跨域推荐提供解释。
3在应用标签改进推荐准确性方面：分别在图模型和潜在因素模型中融合属性标签，提高Top-N推荐的命中率和预测评分推荐的准确度。实验结果表明，相对于经典的协同过滤推荐算法，融合属性标签信息的推荐模型能够提高推荐的准确性。
4在提高推荐算法的效率研究方面：针对现有协同过滤推荐算法的效率问题，分别在聚类模型和奇异值分解模型的基础上，提出了基于概率聚类的预测评分模型和增量式奇异值分解模型。实验验证，相比于经典的推荐算法，本文提出的高效预测评分算法在推荐效率、准确性等方面有较大的优势，可以用于实时推荐。
本文最后通过实现面向读物的Readings推荐服务，验证了群落标签推荐系统架构和相关的算法可以被应用于实际的推荐系统中，能够有效地改进读物推荐的准确性、多样性和效率，提高推荐系统的综合质量。
Along with the development of the information technology and communicationtechnology, and the popularization of internet, how to help people to find the usefulknowledge in vast quantities of information is a problem that is exigent to be solved.The personalized recommendation system is a useful tool to conquer the informationoverload, and helps the users to find the necessary information. The recommendationsystem builds the interesting model through analyzing the characteristics of users(items), and the users’ operating behaviors on items. Based on the models, therecommendation system can predict the users’ preferences to the items, andrecommend the appropriate items. In recent years, the recommendation system hasbeen widely used in industry, such as e-commerce, and researched deeply in theacademic community. However, along with the information requirements of usershave developed into diversification, there are some challenges in recommendationsystem, such as the architecture of recommendation system, the efficiency ofalgorithms, the diversity and accuracy of recommended items. This paper mainlystudies on the architecture of multitasking recommendation system, the models andalgorithms of recommendation based on the collaborative filtering, and verify theiravailability through the standard data sets. The main contributions of our researchare as following:
1. In terms of architecture of recommendation system: our paper designs theprototype of recommendation system, which has5main components, includinginteraction interface, data acquisition and preprocess, behavior analysis and featureextraction, multitasking recommendation module, and feedback analysis. Thepluggable recommendation architecture can choose and deploy the algorithm andmodel according to the users’ requirements. The recommender system can make useof the system architecture and efficient recommending algorithm to get the highaccuracy and diversified recommended items.
2. In terms of improving the diversity of recommended items: we propose the cross domain recommendation based on the cross nearest neighbor model. Throughanalyzing the Folksonomy of different kinds of recommended items, we can minethe association rules in semantics among users’ interests in different domains. Theexperimental results show that our method can implement the cross domainrecommendation and get the association rules among users’ preferences in differentdomains with tags.
3. In terms of improving the accuracy of recommended algorithm: we proposeto integrate the attributes into the graph based model and latent factor model in orderto improve the accuracy of Top-N recommend task and predictive rating task.
4. In terms of improving the efficiency of recommended algorithm: we proposetwo models: one is the probabilistic clustering model based on clustering algorithm,the other is the incremental SVD model based on the singular value decomposealgorithm. The two models can be used to real-time recommendation.
Based on the architecture and algorithms which are introduced in this paper, webuilt the book recommendation system: Readings. Our research satisfies algorithminnovation and engineering practices. Our multitasking recommending architectureand recommending algorithms can improve the effectiveness, accuracy and diversityof recommendation to a large extent.
引文
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