Yi Fang
ABSTRACT
Most recommender systems focus on the areas of leisure activities. As the Web evolves into omnipresent utility, recommender systems penetrate more serious applications such as those in online scientific communities. In this paper, we investigate the task of recommendation in online scientific communities which exhibit two characteristics: 1) there exists very rich information about users and items; 2) The users in the scientific communities tend not to give explicit ratings to the resources, even though they have clear preference in their minds. To address the above two characteristics, we propose matrix factorization techniques to incorporate rich user and item information into recommendation with implicit feedback. Specifically, the user information matrix is decomposed into a shared subspace with the implicit feedback matrix, and so does the item information matrix. In other words, the subspaces between multiple related matrices are jointly learned by sharing information between the matrices. The experiments on the testbed from an online scientific community (i.e., Nanohub) show that the proposed method can effectively improve the recommendation performance.
- M. Balabanović and Y. Shoham. Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3):66--72, 1997. Google Scholar
Digital Library
- D. Goldberg, D. Nichols, B. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61--70, 1992. Google ScholarDigital Library
- A. Gunawardana and C. Meek. A unified approach to building hybrid recommender systems. In 3rd ACM conference on Recommender systems, pages 117--124. ACM, 2009. Google ScholarDigital Library
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In 8th IEEE International Conference on Data Mining, pages 263--272. IEEE, 2008. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009. Google ScholarDigital Library
- Y. Li, J. Hu, C. Zhai, and Y. Chen. Improving one-class collaborative filtering by incorporating rich user information. In 19th ACM International Conference on Information and Knowledge Management, pages 959--968. ACM, 2010. Google ScholarDigital Library
- R. Pan, Y. Zhou, B. Cao, N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In 8th IEEE International Conference on Data Mining, pages 502--511. IEEE, 2008. Google ScholarDigital Library
- I. Pilászy and D. Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In 3rd ACM conference on Recommender systems, pages 93--100. ACM, 2009. Google ScholarDigital Library
- A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In 17th Conference in Uncertainty in Artificial Intelligence, pages 437--444, 2001. Google ScholarDigital Library
- V. Sindhwani, S. Bucak, J. Hu, and A. Mojsilovic. One-class matrix completion with low-density factorizations. In 10th IEEE International Conference on Data Mining, pages 1055--1060. IEEE, 2010. Google ScholarDigital Library
- A. Singh and G. Gordon. Relational learning via collective matrix factorization. In 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 650--658. ACM, 2008. Google ScholarDigital Library
- N. Srebro and T. Jaakkola. Weighted low-rank approximations. In 20th International Conference on Machine Learning, volume 20, page 720, 2003.Google Scholar
Index Terms
- Matrix co-factorization for recommendation with rich side information and implicit feedback
Recommendations
Incremental Matrix Co-factorization for Recommender Systems with Implicit Feedback
WWW '18: Companion Proceedings of the The Web Conference 2018Recommender systems try to predict which items a user will prefer. Traditional models for recommendation only take into account the user-item interaction, usually expressed by explicit ratings. However, in these days, web services continuously generate ...
Matrix factorization for recommendation with explicit and implicit feedback
AbstractMatrix factorization (MF) methods have proven as efficient and scalable approaches for collaborative filtering problems. Numerous existing MF methods rely heavily on explicit feedback. Typically, these data types may be extremely ...
Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the WebThe most popular Recommender systems (RSs) employ Collaborative Filtering (CF) algorithms where users explicitly rate items. Based on these ratings, a user-item rating matrix is generated and used to select the items to be recommended for a target user. ...
Comments