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<!DOCTYPE html>
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<meta name="description" content="RecSys Challenge 2022">
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content="Recommender Systems, RecSys Challenge, Social Media, Dressipi, Fashion recommendations">
<meta name="author" content="RecSysChallenge 2022 Organizers">
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src="./images/ico-twitter.svg" alt="" width="24">
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<h3><a href="http://recsyschallenge.com/2022" style="text-decoration:none;">
RecSys Challenge 2022</a>
</h3>
<div style="margin-top:30px;font-family: Arial,sans-serif">
<ul class="nav nav-pills navbar-nav navbar-left">
<!--
<li role="presentation" class="dropdown"> <a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button" aria-haspopup="true" aria-expanded="false">About <span class="caret"></span>
</a> <ul class="dropdown-menu">
<li><a href="#about">About</a></li> <li><a href="#scenario">Scenario</a></li>
<li><a href="#challenges">Challenges</a></li> <li><a href="#evaluation">Evaluation</a></li>
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<li role="presentation"><a href="#about">About</a></li>
<!--<li role="presentation"><a href="#publications">Publications</a></li>-->
<li role="presentation"><a href="#participation">Participation</a></li>
<li role="presentation"><a href="#dates">Timeline</a></li>
<li role="presentation"><a href="#program">Program</a></li>
<li role="presentation"><a href="#organizers">Organization</a></li>
<li role="presentation"><a href="https://recsys.acm.org/recsys22/" target="\_blank">RecSys 2022</a></li>
</ul>
</div>
</div>
<!-- ABOUT -->
<div id="about" class="lead">
<h1>About<sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h1>
<p>
The RecSys Challenge 2022 is organized by Nick Landia (<a href="https://dressipi.com/" target="_blank">Dressipi</a>), Bruce Ferwerda (<a href="https://ju.se/en" target="_blank">Jönköping University, Sweden</a>), Saikishore Kalloori (<a href="https://ethz.ch/en.html" target="_blank">ETH Zürich, Switzerland</a>), and Abhishek Srivastava (<a href="#" target="_blank">IIM Visakhapatnam, India</a>).
</p>
<br>
<h2>Dressipi<sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h2>
<p>
Dressipi are the fashion-AI experts, providing product and outfit recommendations to leading global retailers.
</p>
<p>
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.
</p>
<p>
Our algorithms enable retailers to make better buying and merchandising decisions by more accurately forecasting product demand and size ratios.
</p>
<p>
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.
</p>
<p>
We work with brands across the US, UK, Europe, and Australia and outperform every competitor when A/B tested.
</p>
<br>
<h2>Challenge Task<sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h2>
<p>
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?
</p>
<p>
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.
</p>
<figure>
<img src="images/session_purchase_data.jpeg"
alt="Example Session and Purchase Data">
<caption>Fig 1: Example Session and Purchase Data</caption>
</figure>
<br>
<p>
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:
<ul>
<li>On average 51% of total visitors are new (Dressipi Data) which means there is no historical data available and we solely have to rely on current session activity. </li>
<li>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.</li>
<li>Sessions can be pretty short so we need to be able to make accurate predictions as early as possible, before the user bounces.</li>
</ul>
</p>
<br>
<h2>Dataset<sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h2>
<p>
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.
</p>
<p>
<ul>
<li>Sessions: The items that were viewed in a session. In this dataset a session is equal to a day, so a session is one user's activity on one day.</li>
<li>Purchases: The purchase that happened at the end of the session. One purchased item per session.</li>
<li>Item features: The label data of items. Things like “color: green,” “neckline: v-neck,” etc.</li>
</ul>
</p>
<p>
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.
</p>
<p>
A more detailed description can be found by clicking on the button below.
</p>
<div class="wrapper">
<a href="dataset.html" target="_blank">
<button type="button" class="btn btn-lg btn-primary button">Dataset description</button>
</a>
</div>
<figure>
<img src="images/image4.png"
alt="Example Content Data">
<caption>Fig 2: Example Content Data</caption>
</figure>
<br>
<h2>Rules<sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h2>
<p>
<ul>
<li>Anyone (18+) can participate</li>
<li>Do not use external data feeds</li>
<li>Do not use models trained on external data</li>
<li>When you train the model, only use the data from the training file. Do not use any data from the "leaderboard" or "final" test files</li>
<li>When predicting, treat each test session independently of all other test sessions (i.e., when predicting for test session B, the model should not have any knowledge of test session A. Even if that came before it in terms of time-stamp)</li>
</ul>
</p>
<p>
Accepted contributions will be presented during the RecSys Challenge Workshop in 2022.
</p>
</div>
<br><br>
<!-- <div id="publications" class="lead">
<h1>Publications</h1>
<p>Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fon, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Delić, Gabriele Sottocornola, Walter Anelli, Nazareno Andrade, Jessie Smith, and Wenzhe Shi. 2020. <a href="https://arxiv.org/abs/2004.13715" target="_blank">Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline</a>, arXiv:2004.13715.</p>
</div> -->
<div id="participation" class="lead">
<h1>Participation and Data<sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h1>
<p>
The data for this year's challenge is provided by Dressipi.
</p>
<!-- <br/> -->
<br>
<div class="wrapper">
<strong><s>Registration & data access is open now!</s></strong><br>
<strong>The challenge has finalized, but you can still access the dataset!</strong><br>
<a href="https://www.dressipi.com/datasets" target="_blank">
<button type="button" class="btn btn-lg btn-primary button"><s>Registration &</s> Data Access</button>
</a>
</div>
<p align="center">Consult the <a target="_blank" href="https://groups.google.com/g/recsys-challenge-2022">Google Groups</a> if you are experiencing any issues.</p>
<br>
<!-- <p>
A strong point of this challenge are the recent regulations on data protection and privacy. The challenge data set will be compliant: if a user deletes a Tweet, or their data from Twitter, the dataset will be promptly updated.
</p> -->
<!-- <br/> -->
<p>
The dataset and detailed information on the challenge participation will be provided after creating an account.
</p>
<p>
<strong>Participation for the challenge is subject to the acceptance of the <a href="Dressipi2022_TC.pdf" target="_blank">Terms & Conditions</a>.</strong>
</p>
</div>
<br><br>
<div id="dates">
<h1>Timeline<sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h1>
<!--
<p>
<font size="5">Note: the timeline is subject to slight modifications.</font>
</p>
-->
<table class="table table-striped lead">
<thead>
<tr>
<th>When?</th>
<th>What?</th>
</tr>
</thead>
<tbody>
<tr>
<td>7 March, 2022</td>
<td>
<strong>Start RecSys Challenge</strong>
<p>Release dataset</p>
</td>
</tr>
<tr>
<td>14 March, 2022</td>
<td>
<strong>Submission System Open</strong>
<p>Leaderboard live</p>
</td>
</tr>
<tr>
<td>14 June, 2022</td>
<td>
<strong>End RecSys Challenge</strong>
</td>
</tr>
<tr>
<td>21 June, 2022</td>
<td>
<strong>Final Leaderboard & Winners</strong>
<p>EasyChair open for submissions</p>
</td>
</tr>
<tr>
<td>28 June, 2022</td>
<td>
<strong>Code Upload</strong>
<p>Upload code of the final predictions</p>
</td>
</tr>
<tr>
<td>14 July, 2022</td>
<td>
<strong>Paper Submission Due</strong>
</td>
</tr>
<tr>
<td>1 August, 2022</td>
<td>
<strong>Paper Acceptance Notifications</strong>
</td>
</tr>
<tr>
<td>14 August, 2022</td>
<td>
<strong>Camera-Ready Papers</strong>
</td>
</tr>
<tr>
<td>TBD</td>
<td>
<strong>RecSys Challenge Workshop</strong>
<p>@ <a href="https://recsys.acm.org/recsys22/">ACM RecSys 2022</a></p>
</td>
</tr>
</tbody>
</table>
</div>
<br><br>
<!-- GUIDELINES -->
<div id="guidelines" class="lead">
<h1>Paper Submission Guidelines<sup><a class="dropup" style="font-size:10px;" href="#top"><span
class="caret"></span> top</a></sup></h1>
<p>
<font color="green"><strong>Submission website:</strong></font> <a target="_blank" href="https://easychair.org/conferences/?conf=recsyschallenge2022">EasyChair</a>
</p>
<ul>
<li>All participants of the challenge are invited to submit if they consider their submission particularly
effective, novel, otherwise interesting, or exploiting identified particularities of the data.
</li>
<li>
<font color="green"><strong>Note:</strong></font> paper submission is mandatory if you want to be
eligible for a prize. Accepted papers are given a presentation slot at the
workshop. At least one author of each accepted paper must attend the workshop and present their work.
Please note that a badly written paper or absence of presence at workshop, may prevent you from being
eligible for the prize. Please contact the workshop organization if none of the authors will be able to
attend the workshop.
</li>
<li>
<font color="green"><strong>Page limit:</strong></font> 7 pages + references (<a
href="https://www.acm.org/publications/proceedings-template">ACM SIG Format</a>)
<!-- <em>Note that this is
in reference to the old two-column format \documentclass[sigconf]{acmart}
</em> -->
Note: This is in reference to the latest single-column ACM template.
</li>
<li>Anonymization of submissions is not required; please include your team name in abstract and text, as
well as a link to your code repository, the achieved score, and a reference to the RecSys Challenge
Website (<a href="http://www.recsyschallenge.com/2022/">http://www.recsyschallenge.com/2022/</a>). Note:
This will be replaced with a reference to an overview paper in the RecSys proceedings for the
camera-ready version.
</li>
<!-- <li>
<font color="green"><strong>Submission website:</strong></font> <a href="https://easychair.org/conferences/?conf=recsyschallenge2020">EasyChair</a>
</li> -->
<li>The submitted papers will be evaluated based on novelty, clarity, and presented empirical results.</li>
<li>Each paper will be reviewed by at least three PC members.</li>
<!-- <li>Accepted papers will be published in the ACM Digital Library.</li> -->
<li>Our proceedings will be published in the ACM Digital Library within its International Conference Proceedings Series.</li>
<li>Accepted papers must be presented in the RecSys Challenge Workshop.</li>
</ul>
<!--
<font color="green"><strong>Link to the workshop website:</strong></font></br>
<a
href="https://recsys.acm.org/recsys19/challenge-workshop/">https://recsys.acm.org/recsys19/challenge-workshop/</a>
-->
</div>
<br><br>
<div class="lead" id="program">
<h1>Workshop Program and Accepted Papers<sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h1>
<em>The RecSys Challenge Workshop will take place on September 22nd, 2022<br> <strong> All times are Pacific Daylight Time</strong></em>
<table class="table table-striped lead">
<tbody>
<tr>
<th>Time</th>
<th> Session </th>
</tr>
<tr><td>14:00<br />-<br /> 14:12</th><th><strong>Opening</strong></td></tr>
<tr><td>14:15<br />-<br /> 14:27</td><td><strong>Lightweight Model for Session-Based Recommender Systems with Seasonality Information in the Fashion Domain</strong> 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</td></tr>
<tr><td>14:30<br />-<br /> 14:42</td><td><strong>United we stand, divided we fall: leveraging ensembles of recommenders to compete with budget constrained resources</strong> Pietro Maldini, Alessandro Sanvito and Mattia Surricchio (virtual)</td></tr>
<tr><td>14:45<br />-<br /> 14:57</td><td><strong>Session-Based Recommendation by combining Probabilistic Models and LSTM</strong> Costas Panagiotakis and Harris Papadakis (virtual)</td></tr>
<tr><td>15:00<br />-<br /> 15:12</td><td><strong>SIHG4SR: Side Information Heterogeneous Graph for Session Recommender</strong> Chendi Xue, Xinyao Wang, Yu Zhou, Ke Ding, Jian Zhang, Rita Brugarolas Brufau and Eric Anderson (virtual)</td></tr>
<tr><td>15:15<br />-<br /> 15:27</td><td><strong>Fashion Recommendation with a real Recommender System Flow</strong> Qi Zhang, Guohao Cai, Wei Guo, Yi Han, Zhenhua Dong, Ruiming Tang and Liangbi Li (virtual)</td></tr>
<tr><td> </td><td> </td></tr>
<tr style="color:gray;"><td>15:30<br />-<br /> 16:00</td><td>Coffee Break</td></tr>
<tr><td> </td><td> </td></tr>
<tr><td>16:00<br />-<br /> 16:12</td><td><strong>Simple and Efficient Recommendation Strategy for Warm/Cold Sessions for RecSys Challenge 2022</strong> Hyunsung Lee, Sungwook Yoo, Andrew Yang, Wonjun Jang and Chiwan Park</td></tr>
<tr><td>16:15<br />-<br /> 16:27</td><td><strong>LightGBM using Enhanced and De-biased Item Representation for Better Session-based Fashion Recommender Systems</strong> Jiangwei Luo, Wenxuan Zhao, Ye Tang, Zhou Zhou, Huimin Xiong and Zhulin Tao (virtual)</td></tr>
<tr><td>16:30<br />-<br /> 16:42</td><td><strong>A Diverse Models Ensemble for Fashion Session-Based Recommendation</strong> Benedikt Schifferer, Jiwei Liu, Sara Rabhi, Gilberto Titericz, Chris Deotte, Gabriel de Souza P. Moreira, Ronay Ak and Kazuki Onodera</td></tr>
<tr><td>16:45<br />-<br /> 16:57</td><td><strong>Session-based Recommendation with Transformer</strong> Yichao Lu, Zhaolin Gao, Zhaoyue Cheng, Jianing Sun, Bradley Brown, Guangwei Yu, Anson Wong, Felipe Pérez and Maksims Volkovs</td></tr>
<tr><td>17:00<br />-<br /> 17:12</td><td><strong>Industrial Solution in Fashion-domain Recommendation by an Efficient Pipeline using GNN and Lightgbm</strong> Zzh, Wei Zhang and Tao Wen (virtual)</td></tr>
<tr><td>17:15<br />-<br /> 17:30</td><td><strong>Closing remarks</td></tr>
</tbody>
</table>
</div>
<!-- ORGANIZERS -->
</br></br>
<div id="organizers" class="lead">
<h1>Organization <sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h1>
<h2>Program Committee <sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h2>
<ul>
<!--<li>Nazareno Andrade, Universidade Federal de Campina Grande</li>-->
<li>Vito Walter Anelli, Politecnico di Bari</li>
<li>Luca Belli, Twitter Cortex</li>
<li>Alejandro Bellogin, Universidad Autonoma de Madrid</li>
<li>Ludovico Boratto, University of Cagliari</li>
<li>Ludovik Coba, Koa Health</li>
<!--<li>Yashar Deldjoo, Polytechnic University of Bari</li>-->
<li>Tommaso Di Noia, Politecnico di Bari</li>
<!--<li>Linus W. Dietz, Technical University of Munich</li>-->
<!--<li>Mehdi Elahi, University of Bergen</li>-->
<!--<li>Bruce Ferwenda, Jönköping University</li>-->
<li>Dietmar Jannach, University of Klagenfurt</li>
<!--<li>Saikishore Kalloori, ETH Zurich</li>-->
<!--<li>Peter Knees, Vienna University of Technology</li>-->
<!--<li>Julia Neidhardt, Vienna University of Technology</li>-->
<!--<li>Frank Portman, Twitter</li>-->
<!--<li>Francesco Ricci, Free University of Bozen-Bolzano</li>-->
<!--<li>Wenzhe Shi, Twitter</li>-->
<!--<li>Gabriele Sottocornola, Libera Università di Bozen-Bolzano</li>-->
<!--<li>Fabio Stella, Dipartimento di Informatica, Sistemistica e Comunicazione, Universita' degli Studi di Milano-Bicocca, Milano, IT.</li>-->
<!--<li>Alykhan Tejani, Twitter</li>-->
<li>Marko Tkalcic, University of Primorska</li>
<li>Wolfgang Wörndl, Technical University of Munich</li>
<!--<li>Markus Zanker, Free University of Bozen-Bolzano</li>-->
</ul>
<br><br>
<h2>Organizers <sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h2>
<ul>
<li><a href="https://www.linkedin.com/in/nicklandia/" target="\_blank">Nick Landia</a>, Dressipi</li>
<li><a href="https://www.bruceferwerda.com" target="\_blank">Bruce Ferwerda</a>, Jönköping University</li>
<li><a href="https://inf.ethz.ch/people/people-atoz/person-detail.MjYxNjY1.TGlzdC8zMDQsLTIxNDE4MTU0NjA=.html" target="\_blank">Saikishore Kalloori</a>, ETH Zürich</li>
<li><a href="#" target="\_blank">Abhishek Srivastava</a>, Indian Institute of Management, Visakhapatnam</li>
<li><a href="https://www.linkedin.com/in/frederickcheung/" target="\_blank">Frederick Cheung</a>, Dressipi</li>
<li><a href="https://www.linkedin.com/in/donnanorth/" target="\_blank">Donna North</a>, Dressipi</li>
</ul>
<br>
<h2>Advisor(s) <sup><a class="dropup" style="font-size:10px;" href="#top"><span class="caret"></span>
top</a></sup>
</h2>
<ul>
<li><a href="http://sisinflab.poliba.it/anelli/" target="\_blank">Vito Walter Anelli</a>, Politecnico di Bari</li>
</ul>
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