The RecSys Challenge 2022 is organized by Nick Landia (Dressipi), Bruce Ferwerda (Jönköping University, Sweden), Saikishore Kalloori (ETH Zürich, Switzerland), and Abhishek Srivastava (IIM Visakhapatnam, India).
Dressipi are the fashion-AI experts, providing product and outfit recommendations to leading global retailers.
Our recommendations enable retailers to create new product discovery experiences that are personalized and inspiring and can be used at all steps of the shopper journey.
Our algorithms enable retailers to make better buying and merchandising decisions by more accurately forecasting product demand and size ratios.
Our focus is to provide the world’s best apparel recommendations and predictions. We do this by taking a domain specific approach across the data we collect and create, how we structure that data and the models we build. Everything we do is optimized to handle the nuances of fashion.
We work with brands across the US, UK, Europe, and Australia and outperform every competitor when A/B tested.
This year’s challenge focuses on fashion recommendations. When given user sessions, purchase data and content data about items, can you accurately predict which fashion item will be bought at the end of the session?
The content data consists of descriptive labels of the items (such as color, length, neckline, sleeve style, 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.
It’s 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:
As part of this challenge, Dressipi will be releasing a public dataset of 1.1 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. The dataset is sampled and anonymized.
The image below is an illustration of what the content data could look like for a given dress (this is a made-up example). In the dataset the label data has been anonymised by using ids: you will not get the cleartext labels like “neckline: v-neck” but rather ids representing the same data.
A more detailed description can be found by clicking on the button below.
Accepted contributions will be presented during the RecSys Challenge Workshop in 2022.
The data for this year's challenge is provided by Dressipi.
Consult the Google Groups if you are experiencing any issues.
The dataset and detailed information on the challenge participation will be provided after creating an account.
Participation for the challenge is subject to the acceptance of the Terms & Conditions.
|7 March, 2022||
Start RecSys Challenge
|14 March, 2022||
Submission System Open
|14 June, 2022||End RecSys Challenge|
|21 June, 2022||
Final Leaderboard & Winners
EasyChair open for submissions
|28 June, 2022||
Upload code of the final predictions
|14 July, 2022||Paper Submission Due|
|1 August, 2022||Paper Acceptance Notifications|
|14 August, 2022||Camera-Ready Papers|
|TBD||RecSys Challenge Workshop|
Submission website: EasyChair
|Lightweight Model for Session-Based Recommender Systems with Seasonality Information in the Fashion Domain Nicola Della Volpe, Lorenzo Mainetti, Alessio Martignetti, Andrea Menta, Riccardo Pala, Giacomo Polvanesi, Francesco Sammarco, Fernando Benjamín Pérez Maurera, Cesare Bernardis and Maurizio Ferrari Dacrema|
|United we stand, divided we fall: leveraging ensembles of recommenders to compete with budget constrained resources Pietro Maldini, Alessandro Sanvito and Mattia Surricchio (virtual)|
|Session-Based Recommendation by combining Probabilistic Models and LSTM Costas Panagiotakis and Harris Papadakis (virtual)|
|SIHG4SR: Side Information Heterogeneous Graph for Session Recommender Chendi Xue, Xinyao Wang, Yu Zhou, Ke Ding, Jian Zhang, Rita Brugarolas Brufau and Eric Anderson (virtual)|
|Fashion Recommendation with a real Recommender System Flow Qi Zhang, Guohao Cai, Wei Guo, Yi Han, Zhenhua Dong, Ruiming Tang and Liangbi Li (virtual)|
|Simple and Efficient Recommendation Strategy for Warm/Cold Sessions for RecSys Challenge 2022 Hyunsung Lee, Sungwook Yoo, Andrew Yang, Wonjun Jang and Chiwan Park|
|LightGBM using Enhanced and De-biased Item Representation for Better Session-based Fashion Recommender Systems Jiangwei Luo, Wenxuan Zhao, Ye Tang, Zhou Zhou, Huimin Xiong and Zhulin Tao (virtual)|
|A Diverse Models Ensemble for Fashion Session-Based Recommendation Benedikt Schifferer, Jiwei Liu, Sara Rabhi, Gilberto Titericz, Chris Deotte, Gabriel de Souza P. Moreira, Ronay Ak and Kazuki Onodera|
|Session-based Recommendation with Transformer Yichao Lu, Zhaolin Gao, Zhaoyue Cheng, Jianing Sun, Bradley Brown, Guangwei Yu, Anson Wong, Felipe Pérez and Maksims Volkovs|
|Industrial Solution in Fashion-domain Recommendation by an Efficient Pipeline using GNN and Lightgbm Zzh, Wei Zhang and Tao Wen (virtual)|