Communication-Based Book Recommendation in Computational Social Systems

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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number6651493
JournalComplexity
Volume2021
DOIs
Publication statusPublished - 2021

ASJC Scopus subject areas

  • Computer Science(all)
  • General

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