DeepAI
Log In Sign Up

Variational Inference for Category Recommendation in E-Commerce platforms

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/02/2020

On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering

Recommender Systems have become an integral part of online e-Commerce pl...
05/31/2016

Extreme Stochastic Variational Inference: Distributed and Asynchronous

We propose extreme stochastic variational inference (ESVI), an asynchron...
06/26/2018

A NoSQL Data-based Personalized Recommendation System for C2C e-Commerce

With the considerable development of customer-to-customer (C2C) e-commer...
06/14/2020

Multi-Purchase Behavior: Modeling and Optimization

We study the problem of modeling purchase of multiple items and utilizin...
01/22/2020

Incentivising Exploration and Recommendations for Contextual Bandits with Payments

We propose a contextual bandit based model to capture the learning and s...
05/13/2020

Multi-modal Embedding Fusion-based Recommender

Recommendation systems have lately been popularized globally, with prima...
05/04/2021

PreSizE: Predicting Size in E-Commerce using Transformers

Recent advances in the e-commerce fashion industry have led to an explor...