利用隐式和显式签名信任关系进行有效推荐

Irfan Ali, Jiwon Hong, Sang-Wook Kim
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引用次数: 6

摘要

信任网络被广泛用于缓解协同过滤中的数据稀疏性和冷启动问题。近年来,人们提出了一些利用显式签名信任关系的方法,即信任和不信任关系。这些方法忽略了这样一个事实,即尽管用户在信任网络中相互信任/不信任,但在现实生活中可能有不同的偏好。这些方法中的大多数也处理不信任和信任的及物性概念。然而,其他已有的研究发现信任是可传递的,而不信任是不可传递的。此外,显式签名信任关系相当稀疏,可能无法推断用户的真实偏好。本文提出建立隐式签名信任关系,并将其与显式签名信任关系结合使用,以解决信任关系的稀疏性问题。我们也确认了相似性(见图2)。内隐信任和外显信任的差异。不信任)关系通过使用用户之间的相似性得分,从而可以推断用户的真实偏好。除此之外,我们还提出了一个矩阵分解模型,该模型同时利用隐式和显式签名信任关系以及评级信息,并处理信任的传递性和不信任的不可传递性。在Epinions数据集上的大量实验表明,该方法在准确率方面优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting implicit and explicit signed trust relationships for effective recommendations
Trust networks have been widely used to mitigate the data sparsity and cold-start problems of collaborative filtering. Recently, some approaches have been proposed which exploit explicit signed trust relationships, i.e., trust and distrust relationships. These approaches ignore the fact that users despite trusting/distrusting each other in a trust network may have different preferences in real-life. Most of these approaches also handle the notion of the transitivity of distrust as well as trust. However, other existing work observed that trust is transitive while distrust is intransitive. Moreover, explicit signed trust relationships are fairly sparse and may not contribute to infer true preferences of users. In this paper, we propose to create implicit signed trust relationships and exploit them along with explicit signed trust relationship to solve sparsity problem of trust relationships. We also confirm the similarity (resp. dissimilarity) of implicit and explicit trust (resp. distrust) relationships by using the similarity score between users so that users' true preferences can be inferred. In addition to these strategies, we also propose a matrix factorization model that simultaneously exploits implicit and explicit signed trust relationships along with rating information and also handles transitivity of trust and intransitivity of distrust. Extensive experiments on Epinions dataset show that the proposed approach outperforms existing approaches in terms of accuracy.
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