# How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead

arXiv: https://arxiv.org/abs/2005.12781 · HTML: https://mlsanity.com/paper/2005.12781/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 6 of 6)

- [Extending CLIP for Category-to-image Retrieval in E-commerce](https://mlsanity.com/paper/2112.11294/) (2021)
- [Query2Prod2Vec Grounded Word Embeddings for eCommerce](https://mlsanity.com/paper/2104.02061/) (2021)
- [Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction](https://mlsanity.com/paper/2104.08874/) (2021)
- [You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack](https://mlsanity.com/paper/2107.07346/) (2021)
- ["Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops](https://mlsanity.com/paper/2107.03256/) (2021)
- [(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis](https://mlsanity.com/paper/2304.11473/) (2023)

## Abstract (arXiv)

In an attempt to balance precision and recall in the search page, leading digital shops have been effectively nudging users into select category facets as early as in the type-ahead suggestions. In this work, we present SessionPath, a novel neural network model that improves facet suggestions on two counts: first, the model is able to leverage session embeddings to provide scalable personalization; second, SessionPath predicts facets by explicitly producing a probability distribution at each node in the taxonomy path. We benchmark SessionPath on two partnering shops against count-based and neural models, and show how business requirements and model behavior can be combined in a principled way.