@inproceedings{tagliabue-etal-2020-grow,
title = "How to Grow a (Product) Tree: Personalized Category Suggestions for e{C}ommerce Type-Ahead",
author = "Tagliabue, Jacopo and
Yu, Bingqing and
Beaulieu, Marie",
editor = "Malmasi, Shervin and
Kallumadi, Surya and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the 3rd Workshop on e-Commerce and NLP",
month = jul,
year = "2020",
address = "Seattle, WA, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ecnlp-1.2/",
doi = "10.18653/v1/2020.ecnlp-1.2",
pages = "7--18",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead
%A Tagliabue, Jacopo
%A Yu, Bingqing
%A Beaulieu, Marie
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 3rd Workshop on e-Commerce and NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, WA, USA
%F tagliabue-etal-2020-grow
%X 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.
%R 10.18653/v1/2020.ecnlp-1.2
%U https://aclanthology.org/2020.ecnlp-1.2/
%U https://doi.org/10.18653/v1/2020.ecnlp-1.2
%P 7-18
Markdown (Informal)
[How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead](https://aclanthology.org/2020.ecnlp-1.2/) (Tagliabue et al., ECNLP 2020)
ACL