Trust-Aware Service Recommendation via Exploiting Social Networks

Mingdong Tang, Yu Xu, Jianxun Liu, Zibin Zheng, Xiaoqing Frank Liu
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引用次数: 18

Abstract

With the rapid growth in the number of available services, recommending suitable services to users becomes increasingly important. A number of collaborative service recommendation methods based on user experiences have been proposed for this purpose. Most of them adopt the similarity-based Collaborative Filtering (CF) technique, which tends to identify similar users for a target user and recommends to the target user the services preferred by the similar users. However, a user similar to the target user is unnecessarily trustworthy to him/her. Therefore, the results recommended by similarity-based CF are probably unreliable. Moreover, existing service recommendation methods seldom incorporate social trust relationships among service users into service recommendation. In this paper, we propose a collaborative, trust-aware service recommendation method for service-oriented environments with social networks. The method is based on an integration of the user-service relation and the user-user social relation. Experimental results demonstrate that our service recommendation method significantly outperforms conventional similarity-based recommendation and trust-based service recommendation methods.
利用社交网络的信任感知服务推荐
随着可用服务数量的快速增长,向用户推荐合适的服务变得越来越重要。为此,人们提出了许多基于用户体验的协同服务推荐方法。它们大多采用基于相似度的协同过滤(CF)技术,该技术倾向于为目标用户识别相似的用户,并向目标用户推荐相似用户喜欢的服务。然而,与目标用户相似的用户对他/她来说是不必要的信任。因此,基于相似度的CF推荐的结果可能不可靠。此外,现有的服务推荐方法很少将服务用户之间的社会信任关系纳入到服务推荐中。在本文中,我们提出了一种基于社交网络的面向服务环境的协作式、信任感知的服务推荐方法。该方法基于用户-服务关系和用户-用户社会关系的集成。实验结果表明,我们的服务推荐方法明显优于传统的基于相似度和基于信任的服务推荐方法。
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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