Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
The Journal of Machine Learning Research
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Matrix Completion from Noisy Entries
The Journal of Machine Learning Research
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Unifying explicit and implicit feedback for collaborative filtering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning to recommend with explicit and implicit social relations
ACM Transactions on Intelligent Systems and Technology (TIST)
Scalable Affiliation Recommendation using Auxiliary Networks
ACM Transactions on Intelligent Systems and Technology (TIST)
Experimental analysis on cross domain preferences association and rating prediction
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
Constrained collective matrix factorization
Proceedings of the sixth ACM conference on Recommender systems
Transfer learning in heterogeneous collaborative filtering domains
Artificial Intelligence
Making recommendations from multiple domains
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross domain recommendation based on multi-type media fusion
Neurocomputing
Personalized recommendation based on review topics
Service Oriented Computing and Applications
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Data sparsity due to missing ratings is a major challenge for collaborative filtering (CF) techniques in recommender systems. This is especially true for CF domains where the ratings are expressed numerically. We observe that, while we may lack the information in numerical ratings, we may have more data in the form of binary ratings. This is especially true when users can easily express themselves with their likes and dislikes for certain items. In this paper, we explore how to use the binary preference data expressed in the form of like/dislike to help reduce the impact of data sparsity of more expressive numerical ratings. We do this by transferring the rating knowledge from some auxiliary data source in binary form (that is, likes or dislikes), to a target numerical rating matrix. Our solution is to model both numerical ratings and like/dislike in a principled way, using a novel framework of Transfer by Collective Factorization (TCF). In particular, we construct the shared latent space collectively and learn the data-dependent effect separately. A major advantage of the TCF approach over previous collective matrix factorization (or bifactorization) methods is that we are able to capture the data-dependent effect when sharing the data-independent knowledge, so as to increase the over-all quality of knowledge transfer. Experimental results demonstrate the effectiveness of TCF at various sparsity levels as compared to several state-of-the-art methods.