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Dynamic collaborative filtering based on user preference drift and topic evolution
2020
IEEE Access
Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users' preferences and item properties dynamically change over time. Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. We
doi:10.1109/access.2020.2993289
fatcat:3ugvw7vqybeztjhvjlwzlc75be