ABSTRACT
Recommender systems make product suggestions that are tailored to the user's individual needs and represent powerful means to combat information overload. In this paper, we focus on the item prediction task of Recommender Systems and present SwarmRankCF, a method to automatically optimize the performance quality of recommender systems using a Swarm Intelligence perspective. Our approach, which is well-founded in a Particle Swarm Optimization framework, learns a ranking function by optimizing the combination of unique characteristics (i.e., features) of users, items and their interactions. In particular, we build feature vectors from a factorization of the user-item interaction matrix, and directly optimize Mean Average Precision metric in order to learn a linear ranking model for personalized recommendations. Our experimental evaluation, on a real world online radio dataset, indicates that our approach is able to find ranking functions that significantly improve the performance of the system for the Top-N recommendation task.
- J. S. Breese, D. Heckerman, and C. M. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Conference on Uncertainty in Artificial Intelligence, 1998. Google ScholarDigital Library
- C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to Rank Using Gradient Descent. In Proceedings of the ICML, 2005. Google ScholarDigital Library
- O. Celma. Music Recommendation and Discovery in the Long Tail. Springer, 2010. Google ScholarCross Ref
- P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the ACM RecSys conference, 2010. Google ScholarDigital Library
- E. Diaz-Aviles, W. Nejdl, and L. Schmidt-Thieme. Swarming to rank for information retrieval. In Proceedings of the ACM GECCO conference, 2009. Google ScholarDigital Library
- Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res., 4:933--969, 2003. Google ScholarDigital Library
- R. Herbrich, T. Graepel, and K. Obermayer. Large Margin Rank Boundaries for Ordinal Regression. In Advances in Large Margin Classifiers, 2000.Google Scholar
- T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the ACM KDD conference, 2002. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42:30--37, August 2009. Google ScholarDigital Library
- T.-Y. Liu. Learning to Rank for Information Retrieval. Springer, 2011.Google ScholarCross Ref
- R. Poli, J. Kennedy, and T. Blackwell. Particle swarm optimization. Swarm Intelligence, 1(1):33--57, 2007.Google ScholarCross Ref
- M. Wetter. Generic Optimization Program -- GenOpt. User Manual,User Manual Version 3.1.0. Lawrence Berkeley National Laboratory., 2011.Google Scholar
Index Terms
- Swarming to rank for recommender systems
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