Variational Inference for Category Recommendation in E-Commerce platforms
This addresses the need for precise and diverse category recommendations to guide user traffic and discovery on e-commerce platforms, though it appears incremental.
The paper tackled the problem of category recommendation in e-commerce by incorporating users' temporal purchase behavior through variational inference, enhanced with Metapath2Vec initialization, and demonstrated improved performance over baselines on two real-world datasets.
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.