RecSys Challenge 2022

The RecSys Challenge 2022 will be organized by Nick Landia (Dressipi), Bruce Ferwerda (Jönköping University, Sweden), Saikishore Kalloori (ETH Zürich, Switzerland), and Abhishek Srivastava (IIM Visakhapatnam, 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.

It is important to be able to make recommendations that respond to what the user is doing during the current session to create the best experience possible that results in a purchase. Nuances of the fashion domain make accurate in-session predictions more critical than in other domains. On average 51% of total visitors are new (Dressipi Data) which means there is no historical data available and we have to rely on current session activity. Even for the other half of visitors that have historical data, trends and other external factors change user preferences much more quickly than in other domains, meaning the historical data might no longer be representative of the user’s interests on a case-to-case basis. This places even more importance on having a highly accurate in-session recommender that can be pulled into the mix. Sessions can be pretty short so we need to be able to make accurate predictions as early as possible, before the user bounces.

A detailed description of the challenge is available on the website of the RecSys Challenge 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 Visakhapatnam
• Frederick Cheung, Dressipi
• Donna North, Dressipi

Advisor

• Vito Walter Anelli, Politecnico di Bari