Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Investigation of various matrix factorization methods for large recommender systems
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the fifth ACM conference on Recommender systems
Time feature selection for identifying active household members
Proceedings of the 21st ACM international conference on Information and knowledge management
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Item Splitting has been proposed as a technique for improving Collaborative Filtering (CF) by means of grouping and exploiting ratings according to the contexts in which they were generated. It shows positive effects on recommendation in the presence of significant differences between the users' preferences within distinct contexts. However, the additional user effort and specific system requirements needed to acquire contextual data may hamper the direct application of the above technique. In this paper we propose to split item sets using a number of time context representations derived from easy-to-collect rating timestamps. Initial results on standard datasets show that the proposed time contexts for item splitting let improve recommendation performance of a state-of-the-art CF algorithm in an offline evaluation setting simulating real-world conditions.