Collaborative Filtering Recommendation Algorithm Based on Rating Prediction and Ranking Prediction
LI Gai1, CHEN Qiang3, LI Lei2
1. Department of Electronic and Information Engineering, Shunde Polytechnic, Foshan, Guangdong 528300, China;
2. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong 510006, China;
3. Department of Computer Science, Guangdong University of Education, Guangzhou, Guangdong 510303, China
Abstract:Collaborative filtering (CF) recommendation algorithm is widely used in the field of e-commerce.The previous researches on CF focused on either rating prediction or ranking prediction.In order to take into account these two aspects,a collaborative filtering recommendation algorithm based on rating prediction and ranking prediction (Unified Recommendation Algorithm,URA) is proposed.URA shares common latent features of users and items in PMF (Probabilistic Matrix Factorization,rating-oriented) and xCLiMF (Extended Collaborative Less-is-More Filtering,ranking-oriented) algorithms,and PMF learns improved latent features of users and items in URA,so that URA improves the performance of ranking recommendation.Experimental results showed that our proposed URA Algorithm outperformed PMF and xCLiMF algorithms over evaluation metrics NDCG and ERR,and that the complexity of URA is shown to be linear with the number of observed ratings.URA is suitable for big data processing in the field of internet information recommendation.
[1] Adomavicius G,Tuzhilin A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extenstions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749.
[2] Ricci F,Rokach L,Shapira B,et al.Recommender System Handbook[M].New York,USA:Springer,2011.
[3] Salakhutdinov R,Mnih A.Probabilistic matrix factorization[A].Proceedings of the 21st Annual Conference on Neural Information Processing Systems[C].Vancouver B C,Canada:ACM,2007.252-260.
[4] Deshpande M,Karypis G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information Systems,2003,22(1):143-177.
[5] Koren Y.Factorization meets the neighborhood:a multifaceted collaborative filtering model[A].Proceedings of the 25th International Conference on Knowledge Discovery and Data Mining[C].Las Vegas:ACM,2008.426-434.
[6] Zhu X W,Ming Z Y,Hao Y,et al.Tackling data sparseness in recommendation using social media based topic hierarchy modeling[A].Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence[C].Buenos Aires,Argentina:ACM,2015.2415-2421.
[7] Li G,Ou W H.Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering[J].Neurocomputing,2016,204:17-25.
[8] Li G,Wang L Y,Ou W H.Robust personalized ranking from implicit feedback[J].International Journal of Pattern Recognition and Artificial Intelligence,2015,30(1):1-28.
[9] Li G,Chen Q.Exploiting explicit and implicit feedbacks for personalized ranking[J].Mathematical Problems in Engineering,2016,2016:1-11.
[10] Liu T Y.Learning to Rank for Information Retrieval[M].Berlin:Springer,2011.
[11] Yao W L,He J,Huang G Y,et al.SoRank:Incorporating social information into learning to rank models for recommendation[A].Proceedings of the 23th ACM International Conference on World Wide Web[C].Seoul,Korea:ACM,2014.409-410.
[12] Weimer M,Karatzoglou A,Le Q V,et al.CofiRank-maximum margin matrix factorization for collaborative ranking[A].Proceedings of the 21th Conference on Advances in Neural Information Processing Systems[C].Vancouver B C,Canada:Curran Associates,Inc,2007.79-86.
[13] Liu N N,Yang Q.Eigenrank:a ranking-oriented approach to collaborative filtering[A].Proceedings of the 31th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Singapore:ACM Press,2008.83-90.
[14] Shi Y,Karatzoglou A,Baltrunas L,et al.xCLiMF:Optimizing expected reciprocal rank for data with multiple levels of relevance[A].Proceedings of the Sixth ACM Conference on Recommender Systems[C].Hongkong:ACM Press,2013.431-433.
[15] Liu N N,Zhao M,Yang Q.Probabilistic latent preference analysis for collaborative filtering[A].Proceedings of ACM International Conference on Information and Knowledge Management[C].Hong Kong,China:ACM,2009.759-766.
[16] Shi Y,Larson M,Hanjalic A.List-wise learning to rank with matrix factorization for collaborative filtering[A].Proceedings of the Fourth ACM Conference on Recommender Systems[C].New York,USA:ACM,2010.269-272.
[17] Shi Y,Larson M,Hanjalic A.Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation[J].Information Sciences,2013,229(6):29-39.