基于社交网络和在线行为分析的推荐增强信任模型

Xue Yu, Zan Wang
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引用次数: 10

摘要

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