Variational Bayesian representation learning for grocery recommendation

Meng, Z. , McCreadie, R. , Macdonald, C. and Ounis, I. (2021) Variational Bayesian representation learning for grocery recommendation. Information Retrieval, 24(4-5), pp. 347-369. (doi: 10.1007/s10791-021-09397-1)

Abstract

Representation learning has been widely applied in real-world recommendation systems to capture the features of both users and items. Existing grocery recommendation methods only represent each user and item by single deterministic points in a low-dimensional continuous space, which limit the expressive ability of their embeddings, resulting in recommendation performance bottlenecks. In addition, existing representation learning methods for grocery recommendation only consider the items (products) as independent entities, neglecting their other valuable side information, such as the textual descriptions and the categorical data of items. In this paper, we propose the Variational Bayesian Context-Aware Representation (VBCAR) model for grocery recommendation. VBCAR is a novel variational Bayesian model that learns distributional representations of users and items by leveraging basket context information from historical interactions. Our VBCAR model is also extendable to leverage side information by encoding contextual features into representations based on the inference encoder. We conduct extensive experiments on three real-world grocery datasets to assess the effectiveness of our model as well as the impact of different construction strategies for item side information. Our results show that our VBCAR model outperforms the current state-of-the-art grocery recommendation models while integrating item side information (especially the categorical features with the textual information of items) results in further significant performance gains. Furthermore, we demonstrate through analysis that our model is able to effectively encode similarities between product types, which we argue is the primary reason for the observed effectiveness gains.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Macdonald, Professor Craig and Ounis, Professor Iadh and Mccreadie, Dr Richard and Meng, Dr Zaiqiao
• Authors: Meng, Z., McCreadie, R., Macdonald, C., and Ounis, I.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Information Retrieval
• Publisher: Springer
• ISSN: 1386-4564
• ISSN (Online): 1573-7659
• Published Online: 27 August 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Information Retrieval 24(4-5): 347-369
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
300332		BigDataStack	Iadh Ounis	European Commission (EC)	779747	Computing Science