Leveraging Review Properties for Effective Recommendation

Wang, X., Ounis, I. and Macdonald, C. (2021) Leveraging Review Properties for Effective Recommendation. In: The Web Conference 2021, Ljubljana, Slovenia, 19-23 April 2021, pp. 2209-2219. ISBN 9781450383127 (doi: 10.1145/3442381.3450038)

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Many state-of-the-art recommendation systems leverage explicit item reviews posted by users by considering their usefulness in representing the users’ preferences and describing the items’ attributes. These posted reviews may have various associated properties, such as their length, their age since they were posted, or their rating of the item. However, it remains unclear how these different review properties contribute to the usefulness of their corresponding reviews in addressing the recommendation task. In particular, users show distinct preferences when considering different aspects of the reviews (i.e. properties) for making decisions about the items. Hence, it is important to model the relationship between the reviews’ properties and the usefulness of the reviews while learning the users’ preferences and the items’ attributes. In this paper, we propose to model the reviews with their associated available properties. We introduce a novel review properties-based recommendation model (RPRM) that learns which review properties are more important than others in capturing the usefulness of reviews, thereby enhancing the recommendation results. Furthermore, inspired by the users’ information adoption framework, we integrate two loss functions and a negative sampling strategy into our proposed RPRM model, to ensure that the properties of reviews are correlated with the users’ preferences. We examine the effectiveness of RPRM using the well-known Yelp and Amazon datasets. Our results show that RPRM significantly outperforms a classical and five existing state-of-the-art baselines. Moreover, we experimentally show the advantages of using our proposed loss functions and negative sampling strategy, which further enhance the recommendation performances of RPRM.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Macdonald, Professor Craig and Ounis, Professor Iadh
Authors: Wang, X., Ounis, I., and Macdonald, C.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2021 IW3C2 (International World Wide Web Conference Committee)
First Published:First published in WWW '21: Proceedings of the Web Conference 2021
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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