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

Jacopo Tagliabue, Bingqing Yu, Marie Beaulieu


Abstract
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.
Anthology ID:
2020.ecnlp-1.2
Volume:
Proceedings of The 3rd Workshop on e-Commerce and NLP
Month:
July
Year:
2020
Address:
Seattle, WA, USA
Venues:
ACL | ECNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–18
Language:
URL:
https://www.aclweb.org/anthology/2020.ecnlp-1.2
DOI:
10.18653/v1/2020.ecnlp-1.2
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ecnlp-1.2.pdf
Video:
 http://slideslive.com/38931242