KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation

Sumit Sidana, Charlotte Laclau, Massih-Reza Amini, Gilles Vandelle, André Bois-Crettez

Abstract: In this paper, we describe a novel, publicly available collection for recommendation systems that records the behavior of customers of the European leader in eCommerce advertising, Kelkoo\footnote{\url{https://www.kelkoo.com/}}, during one month. This dataset gathers implicit feedback, in form of clicks, of users that have interacted with over 56 million offers displayed by Kelkoo, along with a rich set of contextual features regarding both customers and offers. In conjunction with a detailed description of the dataset, we show the performance of six state-of-the-art recommender models and raise some questions on how to encompass the existing contextual information in the system.