Variational Bayesian Context-aware Representation for Grocery Recommendation

Meng, Z. , McCreadie, R. , Macdonald, C. and Ounis, I. (2019) Variational Bayesian Context-aware Representation for Grocery Recommendation. In: 13th ACM Conference on Recommender Systems (RecSys19) - CARS 2019 Workshop, Copenhagen, Denmark, 16-20 Sept 2019,

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Publisher's URL: https://arxiv.org/abs/1909.07705

Abstract

Grocery recommendation is an important recommendation use-case, which aims to predict which items a user might choose to buy in the future, based on their shopping history. However, existing methods only represent each user and item by single deterministic points in a low-dimensional continuous space. In addition, most of these methods are trained by maximizing the co-occurrence likelihood with a simple Skip-gram-based formulation, which limits the expressive ability of their embeddings and the resulting recommendation performance. In this paper, we propose the Variational Bayesian Context-Aware Representation (VBCAR) model for grocery recommendation, which is a novel variational Bayesian model that learns the user and item latent vectors by leveraging basket context information from past user-item interactions. We train our VBCAR model based on the Bayesian Skip-gram framework coupled with the amortized variational inference so that it can learn more expressive latent representations that integrate both the non-linearity and Bayesian behaviour. Experiments conducted on a large real-world grocery recommendation dataset show that our proposed VBCAR model can significantly outperform existing state-of-the-art grocery recommendation methods.

Item Type:Conference Proceedings
Additional Information:BigDataStack has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 77974
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mccreadie, Dr Richard and Meng, Mr Zaiqiao and Ounis, Professor Iadh and Macdonald, Dr Craig
Authors: Meng, Z., McCreadie, R., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in 13th ACM Conference on Recommender Systems (RecSys19) - CARS 2019 Workshop
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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