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

Author

Listed:
  • Zhipeng Zhang

    (Muroran Institute of Technology)

  • Yasuo Kudo

    (Muroran Institute of Technology)

  • Tetsuya Murai

    (Chitose Institute of Science and Technology)

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.

Suggested Citation

  • Zhipeng Zhang & Yasuo Kudo & Tetsuya Murai, 2017. "Neighbor selection for user-based collaborative filtering using covering-based rough sets," Annals of Operations Research, Springer, vol. 256(2), pages 359-374, September.
    • Handle: RePEc:spr:annopr:v:256:y:2017:i:2:d:10.1007_s10479-016-2367-1
    DOI: 10.1007/s10479-016-2367-1
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    References listed on IDEAS

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    1. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
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    Cited by:

    1. Xiao Liao & Guangyu Ye & Juan Yu & Yunjiang Xi, 2021. "Identifying lead users in online user innovation communities based on supernetwork," Annals of Operations Research, Springer, vol. 300(2), pages 515-543, May.

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