基于用户属性的协同滤波混合推荐系统研究
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摘要
协同滤波(Collaborative Filtering,CF)是推荐系统(Recommender System,RS)中最广泛使用的方法之一。而基于用户属性信息的协同滤波方法常用来解决用户冷启动问题。论文中提出了一种混合方法,将基于用户属性信息的协同滤波与传统协同滤波的结果均匀混合为最终的结果。实验表明,混合系统与传统方法相比较,性能得到了明显的提升,且较为稳定。
Collaborative Filtering(CF)is widely used in recommender system. The collaborative filtering method based on user attribute information is often used to solve the problem of user cold start. In this paper,a hybrid method is proposed,which combines the user attribute information with the traditional way. The experimental results show that the performance of the hybrid system is significantly improved and stable compared with the traditional method.
Collaborative Filtering(CF)is widely used in recommender system. The collaborative filtering method based on user attribute information is often used to solve the problem of user cold start. In this paper,a hybrid method is proposed,which combines the user attribute information with the traditional way. The experimental results show that the performance of the hybrid system is significantly improved and stable compared with the traditional method.
引文
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[13]Hu Y,Koren Y,Volinsky C,et al. Collaborative Filtering for Implicit Feedback Datasets[C]//International conference on data mining,2008:263-272.
[14]Koren Y. Collaborative filtering with temporal dynamics[J]. Communications of The ACM,2010,53(4):89-97.
[15]Koren Y,Bell R M,Volinsky C,et al. Matrix Factorization Techniques for Recommender Systems[J]. IEEE Computer,2009,42(8):30-37.
[16]Pirasteh P,Jung J J,Hwang D,et al. Item-Based Collaborative Filtering with Attribute Correlation:A Case Study on Movie Recommendation[C]//Asian conference on intelligent information and database systems,2014:245-252.
[17]Park S,Pennock D M,Madani O,et al. Na?ve filterbots for robust cold-start recommendations[C]//Knowledge discovery and data mining,2006:699-705.
[18] Park S,Chu W. Pairwise preference regression for cold-start recommendation[C]//Conference on recommender systems,2009:21-28.
[19]Bobadilla J,Ortega F,Hernando A. A collaborative filtering similarity measure based on singularities[J]. Information Processing&Management, 2012, 48(2):204-217.
[20]Anand D,Bharadwaj K K. Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities[J]. Expert systems with applications,2011,38(5):5101-5109.
[21]Ahn H J. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem[J].Information Sciences,2008,178(1):37-51.
[2]Mnih A,Salakhutdinov R. Probabilistic Matrix Factorization[C]//Neural information processing systems,2008:1257-1264.
[3]Hofmann T. Latent semantic models for collaborative filtering[J]. ACM Transactions on Information Systems,2004,22(1):89-115.
[4]Koren Y. Factor in the neighbors:Scalable and accurate collaborative filtering[J]. ACM Transactions on Knowledge Discovery From Data,2010,4(1):1-24.
[5]Jahrer M,Toscher A. Collaborative filtering ensemble for ranking[C]//Knowledge discovery and data mining,2011:153-167.
[6]Cremonesi P,Koren Y,Turrin R,et al. Performance of recommender algorithms on top-n recommendation tasks[C]//Conference on recommender systems,2010:39-46.
[7]Liu N N,Yang Q. EigenRank:a ranking-oriented approach to collaborative filtering[C]//International acm sigir conference on research and development in information retrieval,2008:83-90.
[8]Strub F,Gaudel R,Mary J,et al. Hybrid Recommender System based on Autoencoders[J]. ArXiv:Learning,2016:11-16.
[9]Zhang H,Min F,He X,et al. A Hybrid Recommender System Based on User-Recommender Interaction[J].Mathematical Problems in Engineering,2015:1-11.
[10]Strub F,Gaudel R,Mary J,et al. Hybrid Recommender System based on Autoencoders[J]. ArXiv:Learning,2016:11-16.
[11]Herlocker J L,Konstan J A,Borchers A,et al. An algorithmic framework for performing collaborative filtering[C]//International acm sigir conference on research and development in information retrieval,1999:230-237.
[12]Deshpande M,Karypis G. Item-based top-N recommendation algorithms[J]. ACM Transactions on Information Systems,2004,22(1):143-177.
[13]Hu Y,Koren Y,Volinsky C,et al. Collaborative Filtering for Implicit Feedback Datasets[C]//International conference on data mining,2008:263-272.
[14]Koren Y. Collaborative filtering with temporal dynamics[J]. Communications of The ACM,2010,53(4):89-97.
[15]Koren Y,Bell R M,Volinsky C,et al. Matrix Factorization Techniques for Recommender Systems[J]. IEEE Computer,2009,42(8):30-37.
[16]Pirasteh P,Jung J J,Hwang D,et al. Item-Based Collaborative Filtering with Attribute Correlation:A Case Study on Movie Recommendation[C]//Asian conference on intelligent information and database systems,2014:245-252.
[17]Park S,Pennock D M,Madani O,et al. Na?ve filterbots for robust cold-start recommendations[C]//Knowledge discovery and data mining,2006:699-705.
[18] Park S,Chu W. Pairwise preference regression for cold-start recommendation[C]//Conference on recommender systems,2009:21-28.
[19]Bobadilla J,Ortega F,Hernando A. A collaborative filtering similarity measure based on singularities[J]. Information Processing&Management, 2012, 48(2):204-217.
[20]Anand D,Bharadwaj K K. Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities[J]. Expert systems with applications,2011,38(5):5101-5109.
[21]Ahn H J. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem[J].Information Sciences,2008,178(1):37-51.