A Enhanced Trust Model Based on Social Network and Online Behavior Analysis for Recommendation

Xue Yu, Zan Wang
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引用次数: 10

Abstract

The growth of social network applications makes it possible to incorporate relationship information to reason about trust value as many researchers have done with the trust-aware recommendation systems. But without explicit trust rating, inferring trust relationship among users become hard to accomplish. The contribution of this paper includes: 1) constructs user-oriented trust network based on small-worldness property; 2) enhances trust metric with considering social influence of the middle connectors and incorporates users potential relationship estimated from users' interactions frequency; 3) generate a compound weight to re-calculate similarity matrix combined with users social trust relationship.
基于社交网络和在线行为分析的推荐增强信任模型
社交网络应用的发展使得将关系信息纳入信任价值推理成为可能,正如许多研究人员对信任感知推荐系统所做的那样。但如果没有明确的信任等级,就很难对用户之间的信任关系进行推断。本文的贡献包括:1)基于小世界属性构建面向用户的信任网络;2)考虑中间连接器的社会影响,增强了信任度量,并纳入了用户交互频率估计的用户潜在关系;3)生成复合权重,结合用户社会信任关系重新计算相似度矩阵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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