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View all- Patil VChapaneri SJayaswal D(2022)Kernel-Based Matrix Factorization With Weighted Regularization for Context-Aware Recommender SystemsIEEE Access10.1109/ACCESS.2022.319242710(75581-75595)Online publication date: 2022
Contextual factors such as time, location, or tag, can affect user preferences for a particular item. Context-aware recommendations are thus critical to improve both quality and explainability of recommender systems, compared to traditional ...
Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Much of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the ...
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