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Machine Learning - Special issue on inductive transfer
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IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Statistical Models for Co-occurrence Data
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Journal of Artificial Intelligence Research
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ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
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Frontiers of Computer Science in China
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
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Structural context-aware cross media recommendation
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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
Personalized recommendation via cross-domain triadic factorization
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Nonparametric bayesian multitask collaborative filtering
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Cross-domain collaborative filtering via bilinear multilevel analysis
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Cross domain recommendation based on multi-type media fusion
Neurocomputing
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Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately.