@inproceedings{33e88b0812b54d409fc3febe497a459d,
title = "KASANDR: A large-scale dataset with implicit feedback for recommendation",
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, Kelkoo1, 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.",
keywords = "Collection, E-Advertising, Implicit feedback, Recommender systems",
author = "Sumit Sidana and Charlofte Laclau and Amini, \{Massih R.\} and Gilles Vandelle and And{\'r}e Bois-Creftez",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 ; Conference date: 07-08-2017 Through 11-08-2017",
year = "2017",
month = aug,
day = "7",
doi = "10.1145/3077136.3080713",
language = "English",
series = "SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery, Inc",
pages = "1245--1248",
booktitle = "SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval",
}