Song Xu
Shu Wu
ABSTRACT
Recently a ranking view of collaborative recommendation has received much attention in recommendation systems. Most of existing ranking approaches are based on pairwise assumption, i.e., everything that has not been selected is of less interest for a user. However it is usually not proper in many cases. To alleviate the limitation of this assumption, in this work, we present a unified framework, named Personalized Semantic Ranking (PSR). PSR models the personalized ranking and the user-generated content (UGC) simultaneously, and the semantic information extracted from UGC can make a remedy for the pairwise assumption. Moreover, utilizing the semantic information, PSR can capture the more subtle information of the user-item interaction and alleviate the overfitting problem caused by insufficient ratings. The learned topics in PSR can also serve as proper explanations for recommendation. Experimental results show that the proposed PSR yields significant improvements over the competitive compared methods on two typical datasets.
- D. Agarwal and B.-C. Chen. flda: Matrix factorization through latent dirichlet allocation. In WSDM, 2010. Google Scholar
Digital Library
- D. M. Blei, A. Y. Ng, and M. Jordan. Latent dirichlet allocation. JMLR, 2003. Google ScholarDigital Library
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, 2008. Google ScholarDigital Library
- J. Lee, S. Bengio, S. Kim, G. Lebanon, and Y. Singer. Local collaborative ranking. In WWW, 2014. Google ScholarDigital Library
- R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In ICDM, 2008.Google ScholarDigital Library
- S. Rendle. Factorization machines. In ICDM, 2010. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, 2009. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2007.Google ScholarDigital Library
- C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In ACM SIGKDD, 2011. Google ScholarDigital Library
Index Terms
- Personalized Semantic Ranking for Collaborative Recommendation
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