Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Fab: content-based, collaborative recommendation
Communications of the ACM
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Improving one-class collaborative filtering by incorporating rich user information
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
One-Class Matrix Completion with Low-Density Factorizations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Social recommendation across multiple relational domains
Proceedings of the 21st ACM international conference on Information and knowledge management
A latent pairwise preference learning approach for recommendation from implicit feedback
Proceedings of the 21st ACM international conference on Information and knowledge management
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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.