Liang Hu
Abstract
In the real-world environment, users have sufficient experience in their focused domains but lack experience in other domains. Recommender systems are very helpful for recommending potentially desirable items to users in unfamiliar domains, and cross-domain collaborative filtering is therefore an important emerging research topic. However, it is inevitable that the cold-start issue will be encountered in unfamiliar domains due to the lack of feedback data. The Bayesian approach shows that priors play an important role when there are insufficient data, which implies that recommendation performance can be significantly improved in cold-start domains if informative priors can be provided. Based on this idea, we propose a Weighted Irregular Tensor Factorization (WITF) model to leverage multi-domain feedback data across all users to learn the cross-domain priors w.r.t. both users and items. The features learned from WITF serve as the informative priors on the latent factors of users and items in terms of weighted matrix factorization models. Moreover, WITF is a unified framework for dealing with both explicit feedback and implicit feedback. To prove the effectiveness of our approach, we studied three typical real-world cases in which a collection of empirical evaluations were conducted on real-world datasets to compare the performance of our model and other state-of-the-art approaches. The results show the superiority of our model over comparison models.
- B. Li, Q. Yang, and X. Xue. 2009b. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th Annual International Conference on Machine Learning, ACM, 1553454, 617--624. Google Scholar
Digital Library
- N. N. Liu and Q. Yang. 2008. Eigenrank: A ranking-oriented approach to collaborative filtering. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, Singapore2008 ACM, 1390351, 83--90. Google ScholarDigital Library
- M. Long, J. Wang, G. Ding, W. Cheng, X. Zhang, and W. Wang. 2012. Dual transfer learning. In Proceedings of the 12th SIAM International Conference on Data Mining 2012, 540--551.Google Scholar
- B. Loni, Y. Shi, M. Larson, and A. Hanjalic. 2014. Cross-domain collaborative filtering with factorization machines. In Advances in Information Retrieval, M. De Rijke, T. Kenter, A. de Vries, C. Zhai, F. de Jong, K. Radinsky, and K. Hofmann (Eds.). Springer International Publishing, 656--661.Google Scholar
- M. Mørup. 2011. Applications of tensor (multiway array) factorizations and decompositions in data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, 24--40.Google ScholarCross Ref
- P. Niyogi, F. Girosi, and T. Poggio. 1998. Incorporating prior information in machine learning by creating virtual examples. Proc. IEEE 86, 2196--2209.Google ScholarCross Ref
- R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. 2008. One-class collaborative filtering. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 502--511. Google ScholarDigital Library
- W. Pan, E. W. Xiang, N. N. Liu, and Q. Yang. 2010. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the 24th AAAI Conference on Artificial Intelligence 2010. Google ScholarDigital Library
- I. Porteous, A. U. Asuncion, and M. Welling. 2010. Bayesian matrix factorization with side information and dirichlet process mixtures. In AAAI 2010. Google ScholarDigital Library
- S. Rendle. 2010. Factorization machines. In Proceedings of the IEEE 10<sup>th</sup> International Conference on Data Mining (ICDM), 2010, 995--1000. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. 2009a. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada. AUAI Press, 1795167, 452--461. Google ScholarDigital Library
- S. Rendle, L. B. Marinho, A. Nanopoulos, and L. Schmidt-Thieme. 2009b. Learning optimal ranking with tensor factorization for tag recommendation. In Proceedings of the 15th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, ACM, 1557100, 727--736. Google ScholarDigital Library
- S. Rendle and L. Schmidt-Thieme. 2010. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. ACM, 1718498, 81--90. Google ScholarDigital Library
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, ACM, 192905, 175--186. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. 2008. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems 2008, 1257--1264. Google ScholarDigital Library
- R. Salakhutdinov, A. Mnih, and G. Hinton. 2007. Restricted boltzmann machines for collaborative filtering. In Proceedings of the 24th International Conference on Machine Learning. ACM, 1273596, 791--798. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, ACM, 372071, 285--295. Google ScholarDigital Library
- A. I. Schein, A. Popescul, L. H. Ungar and D. M. Pennock. 2002. Methods and metrics for cold-start recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 564421, 253--260. Google ScholarDigital Library
- A. P. Singh and G. J. Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA2008 ACM, 1401969, 650--658. Google ScholarDigital Library
- N. Srebro and T. Jaakkola. 2003. Weighted low-rank approximations. In Proceedings of the 20th International Conference on Machine Learning, 720.Google Scholar
- N. Srebro, J. D. M. Rennie, and T. Jaakkola. 2005. Maximum-margin matrix factorization. In Advances in Neural Information Processing Systems 1329--1336. Google ScholarDigital Library
- X. Su and T. M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 19. Google ScholarDigital Library
- J. Tang, H. Gao, H. Liu, and A. D. Sarma. 2012. Etrust: Understanding trust evolution in an online world. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2339574, 253--261. Google ScholarDigital Library
- K. Train. 2003. Discrete Choice Methods with Simulation. Cambridge University Press.Google Scholar
- P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, ACM, 1390294, 1096--1103. Google ScholarDigital Library
- L. Xiong, X. Chen, T. K. Huang, J. Schneider, and J. G. Carbonell. 2010. Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In Proceedings of SIAM Data Mining 2010.Google Scholar
- Z. H. Zhou and X. Y. Liu. 2010. On multi-class cost-sensitive learning. Computat Intel 26, 232--257.Google ScholarCross Ref
Index Terms
- Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains
Recommendations
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data miningThe goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Jointly modeling content, social network and ratings for explainable and cold-start recommendation
Model-based approach to collaborative filtering (CF), such as latent factor models, has improved both accuracy and efficiency of predictions on large and sparse dataset. However, most existing methods still face two major problems: (1) the ...
Recommendation based on weighted social trusts and item relationships
SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied ComputingRecommender systems have been well studied to overcome the information overload problem in both academia and industry during the past decade. Collaborative filtering is the most popular approach in recommender systems, among which Probabilistic Matrix ...
Comments