* We apologize if you receive multiple copies of this CfP *
* For the online version of this Call, visit:
https://recsys.acm.org/recsys22/challenge/
*
The RecSys Challenge 2022 will be organized by Dressipi (
https://dressipi.com/), Bruce
Ferwerda (Jönköping University, Sweden), Saikishore Kalloori (ETH Zürich, Switzerland),
and Abhishek Srivastava (IIM Jammu, India).
The 2022 RecSys challenge focuses on fashion recommendations; given a sequence of item
views, the label data for those items, and the label data for all candidate items, the
task is to predict the item that was purchased in the session.
As part of the challenge, Dressipi will be releasing a public dataset of 1 million online
retail sessions that resulted in a purchase. In addition all items in the dataset have
been labeled with content data and the labels are supplied. We refer to the label data as
item features (e.g., color, neckline, etc.). The labels have been assigned using
Dressipi’s human-in-the-loop system where fashion experts review, correct and confirm the
correctness of the labels, so we expect this to be a dataset of high accuracy and quality.
The dataset is sampled and anonymized.
In the fashion domain items churn at a very high rate and using content data is essential.
In this challenge a portion of the test purchases we evaluate against will be newer items
that have little or no past interaction data. However, these candidate items will have
label data and can be recommended successfully if we can identify which labels a user
session has a preference for. This is an accurate reflection of what happens in the real
world, where items are available to us to preview and apply content data before they go
live, but when they go live we have to be able to recommend them accurately straight
away.
A detailed description of the challenge is available on the website of the RecSys
Challenge 2022 (
http://www.recsyschallenge.com/2022/). Accepted contributions will be
presented during the RecSys Challenge Workshop in 2022.
CHALLENGE ORGANIZERS
Nick Landia, Dressipi
Bruce Ferwerda, Jönköping University
Saikishore Kalloori, ETH Zürich
Abhishek Srivastava, IIM Jammu
Frederick Cheung, Dressipi
Donna North, Dressipi
ADVISOR
Vito Walter Anelli, Politecnico di Bari