Enhancing Graph Neural Networks for Recommender Systems

Siwei Liu
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引用次数: 2

Abstract

Recommender systems lie at the heart of many online services such as E-commerce, social media platforms and advertising. To keep users engaged and satisfied with the displayed items, recommender systems usually use the users' historical interactions containing their interests and purchase habits to make personalised recommendations. Recently, Graph Neural Networks (GNNs) have emerged as a technique that can effectively learn representations from structured graph data. By treating the traditional user-item interaction matrix as a bipartite graph, many existing graph-based recommender systems (GBRS) have been shown to achieve state-of-the-art performance when employing GNNs. However, the existing GBRS approaches still have several limitations, which prevent the GNNs from achieving their full potential. In this work, we propose to enhance the performance of the GBRS approaches along several research directions, namely leveraging additional items and users' side information, extending the existing undirected graphs to account for social influence among users, and enhancing their underlying optimisation criterion. In the following, we describe these proposed research directions.
用于推荐系统的增强图神经网络
推荐系统是电子商务、社交媒体平台和广告等许多在线服务的核心。为了让用户对所展示的商品保持参与和满意,推荐系统通常会使用用户的历史互动记录,包括他们的兴趣和购买习惯,来进行个性化推荐。近年来,图神经网络(gnn)作为一种能够有效地从结构化图数据中学习表征的技术而出现。通过将传统的用户-物品交互矩阵视为二部图,许多现有的基于图的推荐系统(GBRS)在使用gnn时已经被证明可以达到最先进的性能。然而,现有的GBRS方法仍然存在一些局限性,这阻碍了gnn充分发挥其潜力。在这项工作中,我们建议沿着几个研究方向增强GBRS方法的性能,即利用额外的项目和用户的侧信息,扩展现有的无向图以考虑用户之间的社会影响,并增强其底层优化标准。下面,我们将对这些提出的研究方向进行描述。
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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