Abstract
Recommender systems provide personalized information by learning user preferences. Collaborative filtering (CF) is a common technique widely used in recommendation systems. User-based CF utilizes neighbors of an active user to make recommendations; however, such techniques cannot simultaneously achieve good values for accuracy and coverage. In this study, we present a new model using covering-based rough set theory to improve CF. In this model, relevant items of every neighbor are regarded as comprising a common covering. All common coverings comprise a covering for an active user in a domain, and covering reduction is used to remove redundant common coverings. Our experimental results suggest that this new model could simultaneously present improvements in accuracy and coverage. Furthermore, comparing our model with the unreducted model using all neighbors, our model utilizes fewer neighbors to generate almost the same results.
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Zhang, Z., Kudo, Y., Murai, T. (2015). Applying Covering-Based Rough Set Theory to User-Based Collaborative Filtering to Enhance the Quality of Recommendations. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_27
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DOI: https://doi.org/10.1007/978-3-319-25135-6_27
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