Online social networks gain increasing popularity in recent years. In online social networks, trust prediction is significant for recommendations of high reputation users as well as in many other applications. In the literature, trust prediction problem can be solved by several strategies, such as matrix factorization, trust propagation, and -NN search. However, most of the existing works have not considered the possible complementarity among these mainstream strategies to optimize their effectiveness and efficiency. In this article, we propose a novel trust prediction approach named iSim : an integrated time-aware similarity-based collaborative filtering approach leveraging on user similarity, which integrates three kinds of factors to measure user similarity, including vector space similarity, time-aware matrix factorization, and propagated trust. This article is the first work in the literature employing time-aware matrix factorization and propagated trust in the study of similarity. Additionally, we use several methods like adding inverted index to reduce the time complexity of iSim , and provide its theoretical time bound. Moreover, we also provide the detailed overview and theoretical analysis of the existing works. Finally, the extensive experiments with real-world datasets show that iSim achieves great improvement for both efficiency and effectiveness over the state-of-the-art approaches.

中文翻译：

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具有集成时间感知相似性的在线社交网络的信任预测

近年来，在线社交网络越来越受欢迎。在在线社交网络中，信任预测对于高声誉用户的推荐以及许多其他应用程序具有重要意义。在文献中，信任预测问题可以通过多种策略来解决，例如矩阵分解、信任传播和 -NN 搜索。然而，现有的大部分工作都没有考虑到这些主流策略之间可能存在的互补性，以优化其有效性和效率。在本文中，我们提出了一种新的信任预测方法，名为iSim：一种基于时间感知相似度的集成协同过滤方法，利用用户相似度，它集成了三种因素来衡量用户相似度，包括向量空间相似度、时间感知矩阵分解和传播信任度。本文是文献中第一篇在相似性研究中采用时间感知矩阵分解和传播信任的工作。此外，我们使用了几种方法，例如添加倒排索引来降低时间复杂度iSim，并提供其理论时限。此外，我们还提供了现有作品的详细概述和理论分析。最后，对真实世界数据集的广泛实验表明iSim与最先进的方法相比，效率和有效性都得到了极大的提高。