Abstract
This chapter concerns Recommender systems (RSs), providing personalized information by learning user preferences. User-based collaborative filtering (UBCF) is a significant technique widely utilized in RSs. The traditional UBCF approach selects k-nearest neighbors from candidate neighbors comprised by all users; however, this approach cannot achieve good accuracy and coverage values simultaneously. We present a new approach using covering-based rough set theory to improve traditional UBCF in RSs. In this approach, we insert a user reduction procedure into the traditional UBCF approach. Covering reduction in covering-based rough sets is used to remove redundant users from all users. Then, k-nearest neighbors are selected from candidate neighbors comprised by the reduct-users. Our experimental results suggest that, for the sparse datasets that often occur in real RSs, the proposed approach outperforms the traditional UBCF, and can provide satisfactory accuracy and coverage simultaneously.
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Akama, S., Kudo, Y., Murai, T. (2020). Neighbor Selection for User-Based Collaborative Filtering Using Covering-Based Rough Sets. In: Topics in Rough Set Theory. Intelligent Systems Reference Library, vol 168. Springer, Cham. https://doi.org/10.1007/978-3-030-29566-0_9
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DOI: https://doi.org/10.1007/978-3-030-29566-0_9
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