# Variational Inference for Category Recommendation in E-Commerce platforms

arXiv: https://arxiv.org/abs/2104.07748 · HTML: https://mlsanity.com/paper/2104.07748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [Item2Vec: Neural Item Embedding for Collaborative Filtering](https://mlsanity.com/paper/1603.04259/) (2016)
- [E-commerce in Your Inbox: Product Recommendations at Scale](https://mlsanity.com/paper/1606.07154/) (2016)
- [Edward: A library for probabilistic modeling, inference, and criticism](https://mlsanity.com/paper/1610.09787/) (2016)
- [Deep Probabilistic Programming](https://mlsanity.com/paper/1701.03757/) (2017)
- [Simple, Distributed, and Accelerated Probabilistic Programming](https://mlsanity.com/paper/1811.02091/) (2018)
- [Knowledge-aware Complementary Product Representation Learning](https://mlsanity.com/paper/1904.12574/) (2019)
- [Evaluation Measures for Relevance and Credibility in Ranked Lists](https://mlsanity.com/paper/1708.07157/) (2017)
- [Variational Bayesian Context-aware Representation for Grocery Recommendation](https://mlsanity.com/paper/1909.07705/) (2019)
- [On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering](https://mlsanity.com/paper/2012.01577/) (2020)

## Abstract (arXiv)

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.