Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Comparing Recommendation Strategies in a Commercial Context
IEEE Intelligent Systems
Improving Europeana search experience using query logs
TPDL'11 Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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The recommendation technologies are used as viable solutions for advertising the best products and for helping users to orientate themselves in large e-commerce platforms offering various product assortments. Despite their popularity they still suffer of cold start and sparse data matrices limitations, which affect seriously the effectiveness of recommenders employed in applications with less user-system interaction. Having the aim to improve the quality of recommendation lists in such systems we introduce time heuristics into the recommendation process and propose two new variants of collaborative filtering algorithms for solving these problems. A time aware method is proposed for making more correct evaluations of recommenders used in domains with strong time dependencies.