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arXiv · 2104.09423

SIGIR 2021 E-Commerce Workshop Data Challenge

Jacopo Tagliabue·Ciro Greco·Jean-Francis Roy·Bingqing Yu·Patrick John Chia·Federico Bianchi·Giovanni Cassani
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Abstract

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.

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Jacopo Tagliabue, Ciro Greco, Jean-Francis Roy, Bingqing Yu, Patrick John Chia, Federico Bianchi, Giovanni Cassani. 2021-04-19. SIGIR 2021 E-Commerce Workshop Data Challenge. https://arxiv.org/abs/2104.09423

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