基于用户标签和信任关系的协同过滤推荐算法研究

崔春生,王辉,李群

系统科学与数学 ›› 2019, Vol. 39 ›› Issue (3) : 437-448.

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PDF(704 KB)
系统科学与数学 ›› 2019, Vol. 39 ›› Issue (3) : 437-448. DOI: 10.12341/jssms13604
论文

基于用户标签和信任关系的协同过滤推荐算法研究

    崔春生1,王辉1,李群2
作者信息 +

Research on Collaborative Filtering Recommendation Algorithm Based on User Tags

    CUI Chunsheng1 ,WANG Hui1 ,LI Qun2
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摘要

随着互联网产业的快速发展, 推荐系统已成为商业领域的研究热点, 传统的仅考虑用户相似度或项目相似度的推荐算法已不能满足用户对推荐效率和推荐准确率的要求.考虑到社会好友间信任关系在推荐中的有益作用, 信任关系应当成为推荐系统 的考虑因素之一, 文章提出一种基于标签和信任关系的协同过滤模型.首先, 根据用户标签筛选出相似度较高的用户, 根据他们对项目的评价预测得分;然后, 根据社区内信 任关系计算基于信任的评分;最后, 综合两项得分进行预测. 通过Epinions数据集验证表明: 对比 单纯的相似度推荐, 添加信任因素后推荐结果有明显改变且随着信任网络规模扩大, 项目预测得分趋于稳定, 预测精度明显提高, 更适用于移动电子商务环境下的个性化推荐问题.

Abstract

With the rapid development of the Internet industry, the recommendation system has become a research hotspot in the commercial field. The traditional recommendation algorithm that only considers user similarity or project similarity cannot meet the user's requirements on recommendation efficiency and recommendation accuracy. Considering the beneficial role of trust between social friends in recommendation, trust relationship should be one of the factors to be considered in recommendation system. This paper proposes a collaborative filtering model based on tag and trust relationship. Firstly, users with high similarity were selected according to the user labels, and the score was predicted according to their evaluation of the project. Then, trust-based ratings were calculated based on trust relationships within the community. Finally, the forecast is based on a combination of two scores. Through Epinions data set verification, it is shown that compared with the simple similarity recommendation, the recommendation results have significantly changed after the addition of trust factors. With the expansion of the trust network, the project prediction score tends to be stable and the prediction accuracy is significantly improved, which is more suitable for the personalized recommendation in the mobile e-commerce environment.

关键词

协同过滤算法 /   / 用户标签 /   / 信任关系 /   / 评级预测.

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崔春生 , 王辉 , 李群. 基于用户标签和信任关系的协同过滤推荐算法研究. 系统科学与数学, 2019, 39(3): 437-448. https://doi.org/10.12341/jssms13604
CUI Chunsheng , WANG Hui , LI Qun. Research on Collaborative Filtering Recommendation Algorithm Based on User Tags. Journal of Systems Science and Mathematical Sciences, 2019, 39(3): 437-448 https://doi.org/10.12341/jssms13604
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