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Collaborative filtering recommendation based on trust and emotion

机译：基于信任和情感的协同过滤推荐

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摘要

With the development of personalized recommendations, information overload has been alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of trust still affect the recommendation accuracy in complex social network environments. Additionally, collaborative filtering based on users is vulnerable to shilling attacks due to neighbor preference recommendation. With the objective of overcoming these problems, a collaborative filtering recommendation method based on trust and emotion is proposed in this paper. First, we employ a method based on explicit and implicit satisfaction to alleviate the sparsity problems. Second, we establish trust relationships among users using objective and subjective trust. Objective trust is determined by similarity of opinion, including rating similarity and preference similarity. Subjective trust is determined by familiarity among users based on six degrees of separation. Third, based on the trust relationship, a set of trusted neighbors is obtained for a target user. Next, to further exclude malicious users or attackers from the neighbors, the set is screened according to emotional consistency among users, which is mined from implicit user behavior information. Finally, based on the ratings of items by the screened trusted neighbors and the trust relationships among the target user and these neighbors, we can obtain a list of recommendations for the target user. The experimental results show that the proposed method can improve the recommendation accuracy in the case of data sparsity, effectively resist shilling attacks, and achieve higher recommendation accuracy for cold start users compared to other methods.
机译：随着个性化建议的发展，信息过载被减轻了。然而，用户项目评级矩阵的稀疏性和信任的弱传递仍然影响复杂的社交网络环境中的推荐准确性。此外，基于用户的协作过滤容易受到邻居偏好推荐引起的先令攻击。凭借克服这些问题，本文提出了一种基于信任和情感的协同过滤推荐方法。首先，我们采用了一种基于明确和隐含的满足来缓解稀疏问题的方法。其次，我们建立了使用客观和主观信任的用户之间的信任关系。客观信任由意见的相似性决定，包括评级相似性和偏好相似度。主观信任是通过基于六个分离的用户熟悉确定的。三，基于信任关系，针对目标用户获得一组可信邻居。接下来，为了进一步排除来自邻居的恶意用户或攻击者，可以根据用户之间的情绪一致性筛选该组，从隐式用户行为信息中挖掘。最后，基于屏蔽可信邻居的项目的评级和目标用户和这些邻居之间的信任关系，我们可以获得目标用户的建议列表。实验结果表明，该方法可以提高数据稀疏性的建议准确性，有效地抵抗先令攻击，并与其他方法相比，降低冷启动用户的提高建议准确性。

著录项

  • 来源
    《Journal of Intelligent Information Systems》 |2019年第1期|113-135|共23页
  • 作者

    Guo Liangmin; Liang Jiakun; Zhu Ying; Luo Yonglong; Sun Liping; Zheng Xiaoyao;

  • 作者单位

    Anhui Normal Univ Sch Comp & Informat Wuhu 241003 Peoples R China|Anhui Prov Key Lab Network & Informat Secur Wuhu 241003 Peoples R China;

    Anhui Normal Univ Sch Comp & Informat Wuhu 241003 Peoples R China|Anhui Prov Key Lab Network & Informat Secur Wuhu 241003 Peoples R China;

    Anhui Normal Univ Sch Comp & Informat Wuhu 241003 Peoples R China|Anhui Prov Key Lab Network & Informat Secur Wuhu 241003 Peoples R China;

    Anhui Normal Univ Sch Comp & Informat Wuhu 241003 Peoples R China|Anhui Prov Key Lab Network & Informat Secur Wuhu 241003 Peoples R China;

    Anhui Normal Univ Sch Comp & Informat Wuhu 241003 Peoples R China|Anhui Prov Key Lab Network & Informat Secur Wuhu 241003 Peoples R China;

    Anhui Normal Univ Sch Comp & Informat Wuhu 241003 Peoples R China|Anhui Prov Key Lab Network & Informat Secur Wuhu 241003 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Personalized recommendation; Collaborative filtering; Trust; Emotion; Shilling attack;

    机译：个性化推荐;协作过滤;信任;情绪;先令攻击;

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