An MDP-Based Recommender System
The Journal of Machine Learning Research
Improving maximum margin matrix factorization
Machine Learning
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximum margin matrix factorization for code recommendation
Proceedings of the third ACM conference on Recommender systems
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Using temporal data for making recommendations
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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Past consumption of items affect current choices and influence the perceived quality. The order in which items are consumed can affect the score that a user might give to them. In this work we present two simple models that take advantage of the temporal order of choices and ratings by the user in order to improve the quality of the recommendation. Our model exploits the collaborative effects in the data while also taking into account the order in which items are seen by the users. Experiments show that our approach outperforms standard Matrix Factorization models.