Social Recommendation Based on Implicit Friends Discovering Via Meta-Path

Yuqi Song, Min Gao, Junliang Yu, Qingyu Xiong
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引用次数: 4

Abstract

With the growing popularity of online social platforms, it has been universally recognized that incorporating social relations into recommender systems can usually alleviate the problem of data sparsity. However, social recommender systems based on explicit relations are not as successful as expected due to the noise and the social cold issue of explicit social links. The intuition of utilizing explicit relations is that users share similar preferences if they are friends in the social network. In fact, quite a lot of users who are distant from each other in the social network also have similar tastes. The user item network and the user social network can provide useful information that can complement each other, so that exploring the implicit friends using the heterogeneous network they formed would be more helpful. In this paper, we propose an approach IFSR to discover implicit friends over the heterogeneous network to improve the performance of social recommendation. To find out reliable implicit ties, we first model the system as a heterogeneous network upon which both the preferences and social information are coupled. Over the HIN, similarities between each pair of users can be quantified through network embedding based representation learning. To reduce the computational cost while preserving the information embedded in the original networks and uncover the latent information hiding in the HIN, several meaningful meta-paths over the HIN are designed to guide the process of random walks. Finally, the Top-K implicit friends are incorporated into a social bayesian ranking model to enhance the performance of item ranking. Experimental results on three datasets demonstrate IFSR outperforms the state-of-the-art methods and illustrate why the implicit friends are advantageous for social recommendation.
基于元路径内隐好友发现的社交推荐
随着在线社交平台的日益普及,人们普遍认为将社交关系纳入推荐系统通常可以缓解数据稀疏性的问题。然而,基于显式社会关系的社会推荐系统由于显式社会联系的噪声和社会冷问题,并没有像预期的那样成功。利用显式关系的直觉是,如果用户是社交网络中的朋友,他们会分享相似的偏好。事实上,很多在社交网络上彼此疏远的用户也有着相似的品味。用户项目网络和用户社交网络可以提供互补的有用信息,因此利用它们形成的异构网络来探索隐性朋友将更有帮助。在本文中,我们提出了一种在异构网络中发现隐式朋友的方法,以提高社会推荐的性能。为了找出可靠的隐性联系,我们首先将系统建模为一个异质性网络,在这个网络上,偏好和社会信息都是耦合的。在HIN中,通过基于网络嵌入的表示学习,可以量化每对用户之间的相似度。为了降低计算成本,同时保留原有网络中嵌入的信息,并揭示隐藏在HIN中的潜在信息,在HIN上设计了几个有意义的元路径来指导随机行走过程。最后,将Top-K内隐好友纳入社会贝叶斯排序模型,以提高项目排序的性能。在三个数据集上的实验结果表明,IFSR优于最先进的方法,并说明了为什么内隐朋友有利于社会推荐。
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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