afcarl / vbcar

Variational Bayesian Context-aware Representation for Grocery Recommendation

This repository contains the Python implementation for VBCAR. Further details about VBCAR can be found in our paper:

Variational Bayesian Context-aware Representation for Grocery Recommendation

Meng, Zaiqiao, Richard McCreadie, Craig Macdonald, and Iadh Ounis. "Variational Bayesian Context-aware Representation for Grocery Recommendation." arXiv preprint arXiv:1909.07705 (2019).

Introduction

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.

Requirements

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• pytorch (1.1.0=py3.6_cuda10.0.130_cudnn7.5.1_0)
• python 3.6
• scikit-learn
• scipy Detail package dependencies can be found at myenv.yml

Run the demo

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console demo

python main.py

jupyter notebook demo

• VBCR_example_random_feature.ipynb

If there is any issue with our codes or model, please don't hesitate to let us know.

Citation

If you want to use our codes and datasets in your research, please cite:

@article{meng2019variational,
  title={Variational Bayesian Context-aware Representation for Grocery Recommendation},
  author={Meng, Zaiqiao and McCreadie, Richard and Macdonald, Craig and Ounis, Iadh},
  journal={arXiv preprint arXiv:1909.07705},
  year={2019}
}