@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.",
}