Variational Inference for Category Recommendation in E-Commerce platforms

• URL: http://arxiv.org/abs/2104.07748v2
• Date: Mon, 19 Apr 2021 02:35:14 GMT
• Title: Variational Inference for Category Recommendation in E-Commerce platforms
• Authors: Ramasubramanian Balasubramanian, Venugopal Mani, Abhinav Mathur, Sushant Kumar, Kannan Achan
• Abstract summary: 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.
• Score: 10.64460581091531
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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