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应用科学学报 >

2025 , Vol. 43 >Issue 1: 110 - 122

DOI: https://doi.org/10.3969/j.issn.0255-8297.2025.01.008

计算机应用专辑

基于相对信任增强的推荐算法

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  • 湖南工业大学 轨道交通学院, 湖南 株洲 412007

收稿日期: 2024-07-11

  网络出版日期: 2025-01-24

基金资助

国家自然科学基金（No.62106074）；国家重点研发计划项目（No.2022YFE0103700）；湖南省教育厅科学研究重点项目（No.22A0422）；湖南省青年骨干教师项目（湘教通〔2022〕287号）；中国高校产学研创新基金重点课题（No.2022IT052）资助

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Recommendation Algorithm Based on Relative Trust Enhancement

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  • School of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, Hunan, China

Received date: 2024-07-11

  Online published: 2025-01-24

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摘要

近年来，社会化推荐成为了推荐领域的研究热点。在基于用户历史行为的推荐算法中引入用户的社交关系，能够缓解推荐系统面临的数据稀疏性和冷启动的问题。本文提出了一种基于相对信任增强的推荐算法（relative trust enhancement recommendation algorithm based on the CosRA,RTECosRA）。该算法在“用户-物品”的二部图网络中，基于CosRA相似性指标进行资源分配，在资源分配过程中引入用户的信任关系，调整受信任用户获得的资源值，从而提高受信任用户所选物品的推荐率。在FriendFeed和Epinions数据集上的实验结果显示，相比于基准算法，RTECosRA算法在准确性和多样性上均有提高，且加入信任关系后，扩大了用户的可推荐范围，一定程度上缓解了冷启动问题。

关键词： 社交网络; 推荐算法; 信任关系; 冷启动

本文引用格式

成佳, 陈玲姣, 吴岳忠 . 基于相对信任增强的推荐算法[J]. 应用科学学报, 2025 , 43(1) : 110 -122 . DOI: 10.3969/j.issn.0255-8297.2025.01.008

Abstract

Socialized recommendation has become one of the hot research topics in recent years. Introducing users’ social relationships into the recommendation algorithm based on their historical behavior can alleviate the problems of data sparsity and cold start faced by recommendation systems. This paper proposes a relative trust enhancement recommendation algorithm based on the CosRA (RTECosRA). In the bipartite “user-object” network, this algorithm allocates resources based on the CosRA similarity index and introduces users’ trust relationships during the resource allocation process. It adjusts the resource values obtained by trusted users, thereby increasing the recommendation rate of the items selected by trusted users. The results on the FriendFeed and Epinions datasets show that compared with the baseline algorithms, the RTECosRA algorithm has improvements in both accuracy and diversity. Moreover, by incorporating trust relationships, it expands the recommendable range for users and alleviates the cold start problem to a certain extent.

Key words： social networks; recommender algorithm; trust relationships; cold start

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