Preference Relation Based Matrix Factorization for Recommender Systems

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• 作者：Maunendra Sankar Desarkar (1) maunendra@cse.iitkgp.ernet.in
  Roopam Saxena (1) roopam.saxena@cse.iitkgp.ernet.in
  Sudeshna Sarkar (1) sudeshna@cse.iitkgp.ernet.in
• 关键词：User feedback – ; Preference relations – ; Latent factors – ; Matrix Factorization
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7379
• 期：1
• 页码：63-75
• 全文大小：237.8 KB
• 参考文献：1. Gemmis, M.D., Iaquinta, L., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Preference learning in recommender systems. In: Preference Learning (PL 2009) ECML/PKDD-09 Workshop, pp. 41–55 (2009)
  2. Brun, A., Hamad, A., Buffet, O., Boyer, A.: Towards preference relations in recommender systems. In: Preference Learning (PL 2010) ECML/PKDD 2010 Workshop (2010)
  3. Desarkar, M.S., Sarkar, S., Mitra, P.: Aggregating preference graphs for collaborative rating prediction. In: RecSys 2010, pp. 21–28 (2010)
  4. Jones, N., Brun, A., Boyer, A.: Comparisons instead of ratings: Towards more stable preferences. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 451–456 (2011)
  5. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)
  6. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008, pp. 426–434 (2008)
  7. Pil谩szy, I., Zibriczky, D., Tikk, D.: Fast als-based matrix factorization for explicit and implicit feedback datasets. In: RecSys 2010, pp. 71–78 (2010)
  8. Zhang, L., Agarwal, D., Chen, B.-C.: Generalizing matrix factorization through flexible regression priors. In: RecSys 2011, pp. 13–20 (2011)
  9. Menon, A.K., Chitrapura, K.-P., Garg, S., Agarwal, D., Kota, N.: Response prediction using collaborative filtering with hierarchies and side-information. In: KDD 2011, pp. 141–149 (2011)
  10. Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI 1998, pp. 43–52 (1998)
• 作者单位：1. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Users in recommender systems often express their opinions about different items by rating the items on a fixed rating scale. The rating information provided by the users is used by the recommender systems to generate personalized recommendations for them. Few recent research work on rating based recommender systems advocate the use of preference relations instead of absolute ratings in order to produce better recommendations. Use of preference relations for neighborhood based collaborative recommendation has been looked upon in recent literature. On the other hand, Matrix Factorization algorithms have been shown to perform well for recommender systems, specially when the data is sparse. In this work, we propose a matrix factorization based collaborative recommendation algorithm that considers preference relations. Experimental results show that the proposed method is able to achieve better recommendation accuracy over the compared baseline methods.