Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal collaborative filtering with adaptive neighbourhoods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Applying relevant set correlation clustering to multi-criteria recommender systems
Proceedings of the third ACM conference on Recommender systems
Dynamic updating of online recommender systems via feed-forward controllers
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
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Most of the published approaches to collaborative filtering and recommender systems concentrate on mathematical approaches for identifying user / item preferences. This paper demonstrates that by considering the psychological decision making processes that are being undertaken by the users of the system it is possible to achieve a significant improvement in results. This approach is applied to the Netflix dataset and it is demonstrated that it is possible to achieve a score better than the Cinematch score set at the beginning of the Netflix competition without even considering individual preferences for individual movies. The result has important implications for both the design and the analysis of the data from collaborative filtering systems.