RecSys Challenge 2022

The RecSys Challenge 2022 will be organized by 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.

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. Accepted contributions will be presented during the RecSys Challenge Workshop in 2022.

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