  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.
