Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
The million song dataset challenge
Proceedings of the 21st international conference companion on World Wide Web
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
Expectation-Maximization collaborative filtering with explicit and implicit feedback
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Silence is also evidence: interpreting dwell time for recommendation from psychological perspective
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
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Most collaborative filtering algorithms are based on certain statistical models of user interests built from either explicit feedback (eg: ratings, votes) or implicit feedback (eg: clicks, purchases). Explicit feedbacks are more precise but more difficult to collect from users while implicit feedbacks are much easier to collect though less accurate in reflecting user preferences. In the existing literature, separate models have been developed for either of these two forms of user feedbacks due to their heterogeneous representation. However in most real world recommended systems both explicit and implicit user feedback are abundant and could potentially complement each other. It is desirable to be able to unify these two heterogeneous forms of user feedback in order to generate more accurate recommendations. In this work, we developed matrix factorization models that can be trained from explicit and implicit feedback simultaneously. Experimental results of multiple datasets showed that our algorithm could effectively combine these two forms of heterogeneous user feedback to improve recommendation quality.