Neighbor selection for user-based collaborative filtering using covering-based rough sets

Zhipeng Zhang (), Yasuo Kudo () and Tetsuya Murai ()
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Zhipeng Zhang: Muroran Institute of Technology
Yasuo Kudo: Muroran Institute of Technology
Tetsuya Murai: Chitose Institute of Science and Technology

Annals of Operations Research, 2017, vol. 256, issue 2, No 10, 359-374

Abstract: Abstract Recommender systems (RSs) provide 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 than the traditional UBCF, and can provide satisfactory accuracy and coverage simultaneously.

Keywords: Covering-based rough sets; User-based collaborative filtering; Covering reduction; Active user; Recommender systems (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10479-016-2367-1

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