ABSTRACT
Collaborative filtering algorithms attempt to predict a user's interests based on his past feedback. In real world applications, a user's feedback is often continuously collected over a long period of time. It is very common for a user's interests or an item's popularity to change over a long period of time. Therefore, the underlying recommendation algorithm should be able to adapt to such changes accordingly. However, most existing algorithms do not distinguish current and historical data when predicting the users' current interests. In this paper, we consider a new problem - online evolutionary collaborative filtering, which tracks user interests over time in order to make timely recommendations. We extended the widely used neighborhood based algorithms by incorporating temporal information and developed an incremental algorithm for updating neighborhood similarities with new data. Experiments on two real world datasets demonstrated both improved effectiveness and efficiency of the proposed approach.
- }}J. Abernethy, F. Bach, T. Evgeniou, and J.-P. Vert. A new approach to collaborative filtering: Operator estimation with spectral regularization. Journal of Machine Learning Research,10:803--826, 2009. Google ScholarDigital Library
- }}J. Bennett, C. Elkan, B. Liu, P. Smyth, and D. Tikk. Kdd cup and workshop 2007. SIGKDD Explorations, 9(2):51--52, 2007. Google ScholarDigital Library
- }}Y. Chi, X. Song, D. Zhou, K. Hino, and B. Tseng. Evolutionary spectral clustering by incorporating temporal smoothness. In KDD, pages153--162, 2007. Google ScholarDigital Library
- }}A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW'07: Proceedings of the 16th international conference on World Wide Web, pages 271--280, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- }}Y. Ding and X. Li. Time weight collaborative filtering. In Proc. of CIKM '05, pages 485--492, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- }}W. Fan. Systematic data selection to mine concept-drifting data streams. In KDD, pages 128--137, 2004. Google ScholarDigital Library
- }}J. Gao, W. Fan, J. Han, and P. S. Yu. A general framework for mining concept-drifting data streams with skewed distributions. In SDM, 2007.Google ScholarCross Ref
- }}K. Y. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2):133--151, 2001. Google ScholarDigital Library
- }}J. Herlocker, J. A. Konstan, and J. Riedl. A empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information retrieval, 5(4):287--310, 2002. Google ScholarDigital Library
- }}T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22(1):89--115, 2004. Google ScholarDigital Library
- }}Y. Hu, Y. Koren, and C. Volinsky.Collaborative filtering for implicit feedback datasets. In Proc. of ICDM '08, pages 263--272, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarDigital Library
- }}R. Jin, L. Si, C. Zhai, and J. Callan. Collaborative filtering with decoupled models for preferences and ratings. In Proceedings of CIKM 2003, pages309--106, 2003. Google ScholarDigital Library
- }}Y. Koren. Collaborative filtering with temporal dynamics. In Proc. of SIGKDD 2009, 2009. Google ScholarDigital Library
- }}N. Lathia, S. Hailes, and L. Capra. Temporal collaborative filtering with adaptive neighbourhoods. In Proc. of SIGIR 2009, 2009. Google ScholarDigital Library
- }}M. W. Mahoney, M. Maggioni, and P. Drineas. Tensor-cur decompositions for tensor-based data. In Proc. of KDD '06, pages 327--336, New York, NY, USA, 2006. Google ScholarDigital Library
- }}R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. Proc. of ICDM '08, 0:502--511, 2008. Google ScholarDigital Library
- }}D. M. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles. Collaborative filtering by personality diagnosis: A hybrid memory and model-based approach. In Proc. of UAI, pages 473--480, 2000. Google ScholarDigital Library
- }}J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proc. of ICML '05, pages 713--719, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- }}B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages285--295, 2001. Google ScholarDigital Library
- }}N.S rebro and T. Jaakkola. Weighted low-rank approximations. In ICML, pages 720--727, 2003.Google Scholar
- }}J. Sun, D. Tao, and C. Faloutsos. Beyond streams and graphs: dynamic tensor analysis. In KDD, pages 374--383, 2006. Google ScholarDigital Library
Index Terms
- Online evolutionary collaborative filtering
Recommendations
Probabilistic latent preference analysis for collaborative filtering
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementA central goal of collaborative filtering (CF) is to rank items by their utilities with respect to individual users in order to make personalized recommendations. Traditionally, this is often formulated as a rating prediction problem. However, it is ...
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsUser-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Typicality-Based Collaborative Filtering Recommendation
Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...
Comments