A Heterogeneous Graph Neural Model for Cold-Start Recommendation

Liu, S., Ounis, I. , Macdonald, C. and Meng, Z. (2020) A Heterogeneous Graph Neural Model for Cold-Start Recommendation. In: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), Xi'an, China, 25-30 Jul 2020, pp. 2029-2032. ISBN 9781450380164 (doi: 10.1145/3397271.3401252)

215377.pdf - Accepted Version



The users' historical interactions usually contain their interests and purchase habits based on which personalised recommendations can be made. However, such user interactions are often sparse, leading to the well-known cold-start problem when a user has no or very few interactions. In this paper, we propose a new recommendation model, named Heterogeneous Graph Neural Recommender (HGNR), to tackle the cold-start problem while ensuring effective recommendations for all users. Our HGNR model learns users and items' embeddings by using the Graph Convolutional Network based on a heterogeneous graph, which is constructed from user-item interactions, social links and semantic links predicted from the social network and textual reviews. Our extensive empirical experiments on three public datasets demonstrate that HGNR significantly outperforms competitive baselines in terms of the Normalised Discounted Cumulative Gain and Hit Ratio measures.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Meng, Dr Zaiqiao and Macdonald, Professor Craig and Liu, Siwei and Ounis, Professor Iadh
Authors: Liu, S., Ounis, I., Macdonald, C., and Meng, Z.
College/School:College of Science and Engineering > School of Computing Science
Published Online:25 July 2020
Copyright Holders:Copyright © 2020 Association for Computing Machinery
First Published:First published in SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval: 2029-2032
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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