Communication-Based Book Recommendation in Computational Social Systems

Long Zuo, Shuo Xiong*, Xin Qi, Zheng Wen, Yiwen Tang

*この研究の対応する著者

研究成果: Article査読

抄録

This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users' neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.

本文言語English
論文番号6651493
ジャーナルComplexity
2021
DOI
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 一般

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