Improving maximum margin matrix factorization
Machine Learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Communications of the ACM
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Context-aware movie recommendation based on signal processing and machine learning
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Introduction to special section on CAMRa2010: Movie recommendation in context
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm
Information Systems Frontiers
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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This paper presents our approach to contextual recommendation for the Filmtipset Weekly Recommendation Track of the CAMRA 2010 Challenge[5]. The goal of this task is to predict which items will be rated by each user on specific weeks of the year, namely the week containing Christmas day, and the week leading up to the Oscars, based on ratings collected prior to the test period. Our approach aims at modeling the short-term evolution of the probability that an item is rated before each test period (in a user-independent way), and then forecasting these probabilities on the test week. To that end, we use a temporal regression technique providing non-personalized recommendation with better test performances than other non-personalized recommendation baselines. We then tried, with success, to generate time-dependent collaborative personalized recommendations providing us our best results.