Variational Inference for Category Recommendation in E-Commerce platforms

TL;DR

This paper introduces a variational inference approach combined with Metapath2Vec initialization to improve category recommendations on e-Commerce platforms by leveraging users' purchase repetition behavior, demonstrating superior results on real datasets.

Contribution

The paper proposes a novel combination of variational inference and Metapath2Vec for enhanced category recommendation by modeling user purchase patterns.

Findings

Outperforms standard baseline methods on real datasets

Effectively captures users' purchase repetition behavior

Improves recommendation diversity and accuracy

Abstract

Category recommendation for users on an e-Commerce platform is an important task as it dictates the flow of traffic through the website. It is therefore important to surface precise and diverse category recommendations to aid the users' journey through the platform and to help them discover new groups of items. An often understated part in category recommendation is users' proclivity to repeat purchases. The structure of this temporal behavior can be harvested for better category recommendations and in this work, we attempt to harness this through variational inference. Further, to enhance the variational inference based optimization, we initialize the optimizer at better starting points through the well known Metapath2Vec algorithm. We demonstrate our results on two real-world datasets and show that our model outperforms standard baseline methods.