Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce

Luca Bigon, Giovanni Cassani, Ciro Greco, Lucas Lacasa, Mattia Pavoni, Andrea Polonioli, Jacopo Tagliabue

Research output: Contribution to conferencePosterScientificpeer-review

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Abstract

Knowing if a user is a buyer vs window shopper solely based on clickstream data is of crucial importance for ecommerce platforms seeking to implement real-time accurate NBA (next best action) policies. However, due to the low frequency of conversion events and the noisiness of browsing data, classifying user sessions is very challenging. In this paper, we address the clickstream classification problem in the fashion industry and present three major contributions to the burgeoning field of AI in fashion: first, we collected, normalized and prepared a novel dataset of live shopping sessions from a major European e-commerce fashion website; second, we use the dataset to test in a controlled environment strong baselines and SOTA models from the literature; finally, we propose a new discriminative neural model that outperforms neural architectures recently proposed at Rakuten labs.
Original languageEnglish
Publication statusPublished - 5 Aug 2019
EventAI for fashion - Anchorage, United States
Duration: 5 Aug 20195 Aug 2019
https://kddfashion2019.eu-gb.mybluemix.net

Conference

ConferenceAI for fashion
Abbreviated titleFashionAI
CountryUnited States
CityAnchorage
Period5/08/195/08/19
Internet address

Keywords

  • Intent prediction
  • LSTMs
  • e-commerce

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    Bigon, L., Cassani, G., Greco, C., Lacasa, L., Pavoni, M., Polonioli, A., & Tagliabue, J. (2019). Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce. Poster session presented at AI for fashion, Anchorage, United States.