基于二分网络社团划分的推荐算法
|
摘要
传统的基于用户的协同过滤(User-based CF)推荐算法的推荐效率随着数据的不断增加而降低.本文在User-based CF算法中引入二分网络社团发现理论,提出一种基于二分网络社团划分的推荐算法(RACD).首先通过用户与项目之间的关系建立用户-项目二分网络,然后通过RACD对该网络进行社团划分,得到用户的社团信息,最后通过同一社团中的其他用户对目标用户进行项目的推荐.在经典网络数据集上的实验结果表明,RACD能够有效提高推荐系统实时推荐效率.
The efficiency of traditional user-based collaborative filtering( user-based CF)recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection( RACD) in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly,the user-item rating matrix is mapped into user-item bipartite network. Then, the community information of each user is obtained by using RACD to divide the user-item network. Finally,the items are recommended to the target user according to other users in the same community.Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system.
The efficiency of traditional user-based collaborative filtering( user-based CF)recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection( RACD) in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly,the user-item rating matrix is mapped into user-item bipartite network. Then, the community information of each user is obtained by using RACD to divide the user-item network. Finally,the items are recommended to the target user according to other users in the same community.Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system.
引文
[1]Adomavicius G,Zhang J.Stability of recommendation algorithms[J].ACM Transactions on Information Systems,2010,30(4):47-54.
[2]Schedl M,Hauger D,Farrahi K,et al.On the influence of user characteristics on music recommendation algorithms[C]//European Conference on Information Retrieval.Vienna,2015:339-345.
[3]郑祥云,陈志刚,黄瑞,等.基于主题模型的个性化图书推荐算法[J].计算机应用,2015,35(9):2569-2573.(Zheng Xiang-yun,Chen Zhi-gang,Huang Rui,et al.Personalized book recommendation algorithm based on topic model[J].Journal of Computer Applications,2015,35(9):2569-2573.)
[4]Cai X,Bain M,Krzywicki A,et al.Collaborative filtering for people to people recommendation in social networks[C]//AI2010:Advances in Artificial Intelligence.Berlin:Springer,2010:476-485.
[5]Chee S H S,Han J,Wang K.Rec Tree:an efficient collaborative filtering method[J].Lecture Notes in Computer Science,2001,2114:141-151.
[6]Balabanovi M,Shoham Y.Fab:content-based,collaborative recommendation[J].Communications of the ACM,1997,40(3):66-72.
[7]Lops P,de Gemmis M,Semeraro G.Content-based recommender systems:state of the art and trends[M]//Recommender Systems Handbook.[S.l.]:Springer US,2011:73-105.
[8]Soboroff I M,Nicholas C K.Combining content and collaboration in text filtering[C/OL]//Proceedings of the IJCAI’99 Workshop on Machine Learning in Information Filtering,1999.[2017-01-23].http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.640.3677&rep=rep1&type=pdf.
[9]Girardi R,Marinho L B.A domain model of Web recommender systems based on usage mining and collaborative filtering[J].Requirements Engineering,2007,12(1):23-40.
[10]Papagelis M,Plexousakis D.Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents[J].Engineering Applications of Artificial Intelligence,2005,18(7):781-789.
[11]Zhao Z D,Shang M.User-based collaborative-filtering recommendation algorithms on hadoop[C]//IEEE International Conference on Knowledge Discovery and Data Mining.Phuket,2010:478-481.
[12]孟桓羽,刘真,王芳,等.基于图和改进K近邻模型的高效协同过滤推荐算法[J].计算机研究与发展,2017,54(7):1426-1438.(Meng Huan-yu,Liu Zhen,Wang Fang,et al.An efficient collaborative filtering algorithm based on graph model and improved KNN[J].Journal of Computer Research and Development,2017,54(7):1426-1438.)
[13]Wang Q,Li W,Zhang X,et al.Academic paper recommendation based on community detection in citationcollaboration networks[C]//Asia-Pacific Web Conference:Web Technologies and Applications.Suzhou,2016:124-136.
[14]Chen D M,Yan Y B,Wang D Q,et al.Community detection algorithm based on structural similarity for bipartite networks[C]//Proceedings of 7th IEEE International Conference on Software Engineering and Service Science.Beijing,2016:173-177.
[15]Vozalis M G,Margaritis K G.Using SVD and demographic data for the enhancement of generalized collaborative filtering[J].Information Sciences,2007,177(15):3017-3037.
[2]Schedl M,Hauger D,Farrahi K,et al.On the influence of user characteristics on music recommendation algorithms[C]//European Conference on Information Retrieval.Vienna,2015:339-345.
[3]郑祥云,陈志刚,黄瑞,等.基于主题模型的个性化图书推荐算法[J].计算机应用,2015,35(9):2569-2573.(Zheng Xiang-yun,Chen Zhi-gang,Huang Rui,et al.Personalized book recommendation algorithm based on topic model[J].Journal of Computer Applications,2015,35(9):2569-2573.)
[4]Cai X,Bain M,Krzywicki A,et al.Collaborative filtering for people to people recommendation in social networks[C]//AI2010:Advances in Artificial Intelligence.Berlin:Springer,2010:476-485.
[5]Chee S H S,Han J,Wang K.Rec Tree:an efficient collaborative filtering method[J].Lecture Notes in Computer Science,2001,2114:141-151.
[6]Balabanovi M,Shoham Y.Fab:content-based,collaborative recommendation[J].Communications of the ACM,1997,40(3):66-72.
[7]Lops P,de Gemmis M,Semeraro G.Content-based recommender systems:state of the art and trends[M]//Recommender Systems Handbook.[S.l.]:Springer US,2011:73-105.
[8]Soboroff I M,Nicholas C K.Combining content and collaboration in text filtering[C/OL]//Proceedings of the IJCAI’99 Workshop on Machine Learning in Information Filtering,1999.[2017-01-23].http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.640.3677&rep=rep1&type=pdf.
[9]Girardi R,Marinho L B.A domain model of Web recommender systems based on usage mining and collaborative filtering[J].Requirements Engineering,2007,12(1):23-40.
[10]Papagelis M,Plexousakis D.Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents[J].Engineering Applications of Artificial Intelligence,2005,18(7):781-789.
[11]Zhao Z D,Shang M.User-based collaborative-filtering recommendation algorithms on hadoop[C]//IEEE International Conference on Knowledge Discovery and Data Mining.Phuket,2010:478-481.
[12]孟桓羽,刘真,王芳,等.基于图和改进K近邻模型的高效协同过滤推荐算法[J].计算机研究与发展,2017,54(7):1426-1438.(Meng Huan-yu,Liu Zhen,Wang Fang,et al.An efficient collaborative filtering algorithm based on graph model and improved KNN[J].Journal of Computer Research and Development,2017,54(7):1426-1438.)
[13]Wang Q,Li W,Zhang X,et al.Academic paper recommendation based on community detection in citationcollaboration networks[C]//Asia-Pacific Web Conference:Web Technologies and Applications.Suzhou,2016:124-136.
[14]Chen D M,Yan Y B,Wang D Q,et al.Community detection algorithm based on structural similarity for bipartite networks[C]//Proceedings of 7th IEEE International Conference on Software Engineering and Service Science.Beijing,2016:173-177.
[15]Vozalis M G,Margaritis K G.Using SVD and demographic data for the enhancement of generalized collaborative filtering[J].Information Sciences,2007,177(15):3017-3037.