An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A comparative study of heterogeneous item recommendations in social systems
Information Sciences: an International Journal
Exploiting time contexts in collaborative filtering: an item splitting approach
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Understanding and improving relational matrix factorization in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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The use of temporal dynamic terms in Matrix Factorization (MF) models of recommendation have been proposed as a means to obtain better accuracy in rating prediction task. However, the way such models have been tested may not be a realistic setting for recommendation. In this paper, we evaluated rating prediction and top-N recommendation tasks using a MF model with and without temporal dynamic terms under two evaluation settings. Our experiments show that the addition of dynamic parameters do not necessarily yield to better results on these tasks when a more strict time-aware separation of train/test data is performed, and moreover, results may vary notably when different evaluation schemes are used.