A Modified PMF Model Incorporating Implicit Item Associations
作者： Qiang Liu , Chengwei Wang , Congfu Xu
关键词: Factor analysis 、 Collaborative filtering 、 Machine learning 、 Recommender system 、 Artificial intelligence 、 Data mining 、 Computer science 、 Probabilistic logic 、 Regularization (mathematics) 、 Matrix decomposition 、 Context model
摘要: As a state-of-the-art recommendation technique, collaborative filtering (CF) methods compute recommendations by leveraging historical data set of users' ratings for items. So far, the best performing CF are latent factor models. Probabilistic matrix factorization (PMF) model, as widely used offers probabilistic foundation regularization. In this paper, we present novel method incorporating implicit relationship between items into basic PMF model. Firstly mine correlation based on model utilizing contextual information, and then generalize obtained item We validate our approach two datasets, experimental results show that proposed outperforms several existing
acm.org
sci-hub.se 参考文章(10)
Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Context-Aware Recommender Systems Ai Magazine. ,vol. 32, pp. 67- 80 ,(2011) , 10.1609/AIMAG.V32I3.2364
Hao Ma, Michael R. Lyu, Irwin King, Learning to recommend with trust and distrust relationships conference on recommender systems. pp. 189- 196 ,(2009) , 10.1145/1639714.1639746
Yehuda Koren, Factorization meets the neighborhood Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08. pp. 426- 434 ,(2008) , 10.1145/1401890.1401944
Yehuda Koren, Robert Bell, Chris Volinsky, Matrix Factorization Techniques for Recommender Systems IEEE Computer. ,vol. 42, pp. 30- 37 ,(2009) , 10.1109/MC.2009.263
Hao Ma, Irwin King, Michael R. Lyu, Learning to recommend with social trust ensemble international acm sigir conference on research and development in information retrieval. pp. 203- 210 ,(2009) , 10.1145/1571941.1571978
Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, Nuria Oliver, Multiverse recommendation Proceedings of the fourth ACM conference on Recommender systems - RecSys '10. pp. 79- 86 ,(2010) , 10.1145/1864708.1864727
Andriy Mnih, Ruslan R Salakhutdinov, Probabilistic Matrix Factorization neural information processing systems. ,vol. 20, pp. 1257- 1264 ,(2007)
G. Linden, B. Smith, J. York, Amazon.com recommendations: item-to-item collaborative filtering IEEE Internet Computing. ,vol. 7, pp. 76- 80 ,(2003) , 10.1109/MIC.2003.1167344
Martin Ester, Mohsen Jamali, A transitivity aware matrix factorization model for recommendation in social networks international joint conference on artificial intelligence. pp. 2644- 2649 ,(2011) , 10.5591/978-1-57735-516-8/IJCAI11-440
Michele Gorgoglione, Umberto Panniello, A Contextual Modeling Approach to Context-Aware Recommender Systems ,(2011)