Graph neural pre-training for recommendation with side information

Liu, S., Meng, Z. , Macdonald, C. and Ounis, I. (2023) Graph neural pre-training for recommendation with side information. ACM Transactions on Information Systems, 41(3), 74. (doi: 10.1145/3568953)

Abstract

Leveraging the side information associated with entities (i.e. users and items) to enhance the performance of recommendation systems has been widely recognized as an important modelling dimension. While many existing approaches focus on the integration scheme to incorporate entity side information -- by combining the recommendation loss function with an extra side information-aware loss -- in this paper, we propose instead a novel pre-training scheme for leveraging the side information. In particular, we first pre-train a representation model using the side information of the entities, and then fine-tune it using an existing general representation-based recommendation model. Specifically, we propose two pre-training models, named GCN-P and COM-P, by considering the entities and their relations constructed from side information as two different types of graphs respectively, to pre-train entity embeddings. For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks. For the COM-P model, two multi-relational graphs are constructed to pre-train the entity representations by using the Composition-based Graph Convolutional Networks. An extensive evaluation of our pre-training models fine-tuned under four general representation-based recommender models, i.e. MF, NCF, NGCF and LightGCN, shows that effectively pre-training embeddings with both the user's and item's side information can significantly improve these original models in terms of both effectiveness and stability.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Meng, Dr Zaiqiao and Ounis, Professor Iadh and Macdonald, Professor Craig and Liu, Siwei
• Authors: Liu, S., Meng, Z., Macdonald, C., and Ounis, I.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: ACM Transactions on Information Systems
• Publisher: ACM Press
• ISSN: 1046-8188
• ISSN (Online): 1558-2868
• Published Online: 02 December 2022
• Copyright Holders: Copyright © 2023 Association for Computing Machinery.
• First Published: First published in ACM Transactions on Information Systems 41(3):74
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Related URLs

• arXiv