GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Collaborative Filtering Using Orthogonal Nonnegative Matrix Tri-factorization
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Recommendations Over Domain Specific User Graphs
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Recommendation based on object typicality
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
Co-clustering with augmented data matrix
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
A generic semantic-based framework for cross-domain recommendation
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Transfer learning to predict missing ratings via heterogeneous user feedbacks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
User-based collaborative filtering on cross domain by tag transfer learning
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
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
TALMUD: transfer learning for multiple domains
Proceedings of the 21st ACM international conference on Information and knowledge management
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
Personalized recommendation via cross-domain triadic factorization
Proceedings of the 22nd international conference on World Wide Web
Nonparametric bayesian multitask collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Co-clustering with augmented matrix
Applied Intelligence
Collaborative filtering based on rating psychology
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Collaborative filtering using multidimensional psychometrics model
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Bilevel visual words coding for image classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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
Expert Systems with Applications: An International Journal
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The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring useritem rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods.