Enhancing Graph Neural Networks for Recommender Systems

Liu, S. (2020) Enhancing Graph Neural Networks for Recommender Systems. In: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), Xi'an, China, 25-30 Jul 2020, p. 2484. ISBN 9781450380164 (doi: 10.1145/3397271.3401456)

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Siwei
• Authors: Liu, S.
• College/School: College of Science and Engineering
• ISBN: 9781450380164
• Published Online: 25 July 2020
• Copyright Holders: Copyright © 2020 The Author
• First Published: First published in SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval: 2484
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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