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Effect of distrust propagation to enhance the performance of trust based recommender systems

机译：不信任传播对增强基于信任的推荐系统的性能的影响

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Trust aware recommender systems (TARS) are a branch of the most popular technique of recommender systems that is Collaborative Filtering. Lots of studies used trust as an enhancement factor for improve the accuracy of TARS, but the main problem of using trust is sparsity and also scalability problem for updating explicit data for new users, trust propagation used as a solution for solving the problem, but recent studies shows that using distrust values can also be useful, but same as trust and even worse, sparsity is a critical problem of dataset for using distrust. In this paper we used friend of friend and enemy of enemy concept as a way for propagating distrust and exploit new implicit relations for solving the sparsity problem and also improving the accuracy of recommendations. The results shows that propagating of distrust values is beneficial in enhancement of trust ware recommender systems.
机译：信任感知推荐系统（TARS）是推荐系统最流行的技术的一个分支，即协作过滤。许多研究使用信任作为提高TARS准确性的增强因素，但是使用信任的主要问题是稀疏性以及用于为新用户更新显式数据的可伸缩性问题，信任传播被用作解决问题的解决方案，但是最近研究表明，使用不信任值也可能有用，但与信任相同，更糟糕的是，稀疏性是使用不信任的数据集的关键问题。在本文中，我们使用“朋友的朋友”和“敌人的敌人”概念来传播不信任，并利用新的隐式关系来解决稀疏性问题并提高建议的准确性。结果表明，传播不信任值有利于增强信任软件推荐系统。

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