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于社群网路上结合共同评价及信任关联以进行协同推荐

【中文摘要】:近年来在web-based社群网路上的推荐系统当中,加入使用者之间的信任关联以产生更好的推荐绩效,已经是一种非常普遍的方法。然而随着各种线上社群网路的兴起,使用者之间的关联也不再侷限于单纯的友好或正向信任关联。许多的线上社群网站都在使用者的关联上加入了负向信任的机制,如:Epinions.com可设定其他使用者为白名单(Trust)或黑名单(Distrust)、Slashdot.org可将其他使用者设定为朋友或敌人等等。因此,使用者之间的负向关联对于推荐系统的影响具有其重要性。但在过去对于利用信任的传递性找出使用者之间隐含关联的研究上,主要都着重于探讨正向信任关联的传递。而在负向信任的传递研究上,普遍认为负向信任的传递不如正向信任关联单纯,例如:我们能够理解对于朋友的朋友也具有友好关联,但却难以辨认敌人的敌人是敌是友。因此认为负向信任无法像正向信任关联般传递

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本研究根据社会心理学的结构平衡理论当中的稳定结构,提出符合负向信任传递的条件与方法,并设定多种实验情境,各别测试正负向信任对于推荐系统绩效的影响。除了使用者之间相对性的区域信任关联外,本研究亦加入了属于全域性信任度的In-Degree值进行实验与比较,并利用信任值与In-Degree权重对于K个最近邻居法的相似度进行调整,更利用K个最近邻居法的TOP K邻居,将In-Degree权重与信任值应用到矩阵分解演算法当中,有效针对矩阵分解演算法中的预

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测评分公式进行合理的修正。
根据实验结果,可以看出负向信任在使用本研究提出的方法上,能够成功传递其负向信任值,且对于推荐系统绩效亦有帮助。而In-Degree值的影响也在考量负向In-Degree值的结合下有较佳的表现。
【英文摘要】:It becomes a more and more popular approach to develop recommendation systems based on trust relationships on the social networks. While social information that explicitly indicate the trust relationships among users has been used, some recent studies have started to use the transitivity of trust relationships to find the hidden relationships. However, the latter trend mainly makes use of the positive trust relationship; it considers distrust relationships as not transitive and thus not applicable to the recommendation systems.
Based on

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the structural balance theory of social psychology, in this thesis, I propose a new method that exploits the transitivity of distrust relationships with the positive trust relationships, and analyze the conditions for using these relationships in making recommendations. The proposed method integrates both of the trust and distrust values derived from the social networks and the user-similarities measured from the co-ratings of different users on items. With the hybrid measurements, the collaborative filtering techniques, including the k-nearest neighbor algorithm and the matrix factorization algorithm are used to predict the user preferences of un-rated items. A series of experiments have been conducted and the results show that the distrust relationships and the transitive effect among users can improve the performance of recommendation.
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  • 来源:中山大学;作者:马传渊
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