Communication-Based Book Recommendation in Computational Social Systems. (30th January 2021)

• Record Type: Journal Article
• Authors: Zuo, Long; Xiong, Shuo; Qi, Xin; Wen, Zheng; Tang, Yiwen
• Other Names: Wang Wei Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-01-30
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/6651493
• Languages: English
• ISSNs: 1076-2787

Abstract

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.