<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=Windows-1252">
<style type="text/css" style="display:none;"> P {margin-top:0;margin-bottom:0;} </style>
</head>
<body dir="ltr">
<div class="x_elementToProof" style="font-size:12pt;font-family:Calibri, Arial, Helvetica, sans-serif;margin:0px;color:rgb(0, 0, 0);background-color:rgb(255, 255, 255)">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
<span class="x_ContentPasted0 elementToProof" style="font-weight:700;margin:0px">Call for Papers: </span><span class="x_ContentPasted0" style="font-weight:700;margin:0px">Workshop on </span><b class="x_ContentPasted0">Machine Learning for Streaming Media at
The WebConf2023<br class="x_ContentPasted0">
</b>Austin, Texas, USA, April 30, 2023.<br class="x_ContentPasted0">
<a href="https://ml4streamingmedia-workshop.github.io/www/index.html" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" class="x_ContentPasted0" data-safelink="true" data-linkindex="0" style="margin:0px;color:rgb(41, 98, 255)">https://ml4streamingmedia-workshop.github.io/www/index.html</a> <br class="x_ContentPasted0">
</p>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
<span class="x_ContentPasted0" style="font-weight:700;margin:0px"><br>
</span></p>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
<span class="x_ContentPasted0" style="font-weight:700;margin:0px">Submission deadline:<span class="x_ContentPasted0" style="margin:0px"> </span></span>6th of February 2023</p>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
<br>
</p>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
Streaming media have been seeing massive year over year growth in terms of consumption hours recently. For many people, streaming services have become part of everyday life and accessing and consuming media content via streaming is now the norm for people of
all ages. Powered by Machine Learning (ML) algorithms, streaming services are becoming one the most visible and impactful applications of ML that directly interact with people and influence their lives.<span style="font-weight:700;margin:0px"><br class="x_ContentPasted0">
</span></p>
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
Despite the rapid growth of streaming services, the research discussions around ML for streaming media remain fragmented across different conferences and workshops. Also, the gap between academic research and constraints and requirements in industry limits
the broader impact of many contributions from academia. Therefore, we believe that there is an urgent need to: (i) build connections and bridge the gap by bringing together researchers and practitioners from both academia and industry working on these problems,
(ii) attract ML researchers from other areas to streaming media problems, and (iii) bring up the pain points and battle scars in industry to which academia researchers can pay more attention.</p>
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
With this motivation in mind, we are organizing a workshop on Machine Learning for Streaming Media in conjunction with the WebConf 2023. We invite quality research contributions, including original research, preliminary research results, and proposals for new
work, to be submitted. All submitted papers will be peer reviewed by the program committee and judged for their relevance to the workshop, especially to the topics identified below, and their potential to generate discussion. Accepted submissions will be presented
at the workshop and will be published in the companion (workshop) proceedings of the WebConf 2023. We welcome research that has been previously published or is under review elsewhere. Such articles should be clearly identified at the time of submission and
will not be published in the proceedings.</p>
</div>
<div class="x_elementToProof" style="font-size:12pt;font-family:Calibri, Arial, Helvetica, sans-serif;margin:0px;color:rgb(0, 0, 0);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)"><u class="x_ContentPasted0"><br>
</u></span></div>
<div class="x_elementToProof" style="font-size:12pt;font-family:Calibri, Arial, Helvetica, sans-serif;margin:0px;color:rgb(0, 0, 0);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)"><u class="x_ContentPasted0">Workshop Topics</u></span></div>
<div class="x_elementToProof" style="font-size:12pt;font-family:Calibri, Arial, Helvetica, sans-serif;margin:0px;color:rgb(0, 0, 0);background-color:rgb(255, 255, 255)">
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
<br>
</p>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
The main topics we would like to consider for this workshop are</p>
<ul style="color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
<li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Content Understanding</p>
<ul>
<li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Multimodal representation</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Feature extraction for audio, video, and image content</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Knowledge Graph generation for streaming media</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Semi-supervised learning for content understanding</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Metadata enrichment for music, podcast, video catalog</p>
</li></ul>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Search and recommendation for streaming media</p>
<ul>
<li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Named entity recognition (e.g. identifying celebrities, hosts, artists)</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Conversational systems</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Reward modeling and shaping</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Item cold start problems and challenges</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Designing scalable ML systems</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Heterogeneous content recommendation</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Learning to rank</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Transfer learning</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Explainable recommendations</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Representation learning</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Graph learning algorithms for streaming media</p>
</li></ul>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Measurement, Metrics & Evaluation</p>
<ul>
<li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Evaluation methodologies for streaming media search and recommendations</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Methodologies for valuation of content</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Measuring business impact of recommendation systems</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Life-time value modeling</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Churn prediction & retention modeling</p>
</li></ul>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">User Studies & Human-In the Loop</p>
<ul>
<li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">User studies on real-world recommenders – Human-In the loop recommendations</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Mixed methods research</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">User studies on preference elicitation</p>
</li></ul>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Trust, Safety & Algorithmic Fairness</p>
<ul>
<li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Identifying misinformation and disinformation – Algorithmic fairness in recommendations</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Hate-speech and fake news detection</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Content moderation</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Societal impact of recommendation systems for streaming media</p>
</li></ul>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Machine learning to optimize streaming quality of experience</p>
</li></ul>
<span dir="ltr" style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)"></span><u style="color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)"><span dir="ltr" class="x_ContentPasted0" style="margin:0px">Important
Dates</span><br class="x_ContentPasted0">
</u>
<ul style="color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
<li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px"><span class="x_ContentPasted0" style="font-weight:700;margin:0px">Submission deadline: 6th of February 2023</span> </p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Author notification: 6th of March 2023</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Camera-ready version deadline: 20th of March 2023</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px">Workshop: 30th of April 2023</p>
</li></ul>
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px;color:rgba(0, 0, 0, 0.87);font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;font-size:14px;background-color:rgb(255, 255, 255)">
All deadlines are 11:59 pm, <a href="https://en.wikipedia.org/wiki/Anywhere_on_Earth" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" class="x_ContentPasted0" data-loopstyle="link" data-safelink="true" data-linkindex="1" style="margin:0px;color:rgb(41, 98, 255)"><span style="margin:0px;text-decoration-line:underline">Anywhere
on Earth</span></a> (AoE).</p>
</div>
<div class="x_elementToProof" style="font-size:12pt;font-family:Calibri, Arial, Helvetica, sans-serif;margin:0px;color:rgb(0, 0, 0);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)"><u class="x_ContentPasted0"><br>
</u></span></div>
<div class="x_elementToProof" style="font-size:12pt;font-family:Calibri, Arial, Helvetica, sans-serif;margin:0px;color:rgb(0, 0, 0);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)"><u class="x_ContentPasted0">Submission Instructions</u></span></div>
<div class="x_elementToProof" style="font-size:12pt;font-family:Calibri, Arial, Helvetica, sans-serif;margin:0px;color:rgb(0, 0, 0);background-color:rgb(255, 255, 255)">
<div style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="margin:0px"><br class="x_ContentPasted0">
</span></div>
<div class="x_elementToProof" style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)">
<span dir="ltr" class="x_ContentPasted0" style="margin:0px">Submission link: <a href="https://easychair.org/conferences/?conf=thewebconf2023iwpd" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" class="x_ContentPasted0" data-loopstyle="link" data-safelink="true" data-linkindex="2" style="margin:0px;color:rgb(41, 98, 255)"><span style="margin:0px;text-decoration-line:underline">https://easychair.org/conferences/conf=thewebconf2023iwpd</span></a> </span></div>
<div style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="margin:0px"><br class="x_ContentPasted0">
</span></div>
<div style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="margin:0px"><u class="x_ContentPasted0">Formatting Instructions</u></span>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px">
<br>
</p>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px">
Submissions should not exceed six pages in length (including appendices and references). Papers must be submitted in PDF format according to the ACM template published in the ACM guidelines, selecting the generic “sigconf” sample. The PDF files must have all
non-standard fonts embedded. Workshop papers must be self-contained and in English.</p>
<span dir="ltr" style="margin:0px"><u class="x_ContentPasted0 x_elementToProof">
<div style="margin:0px;background-color:rgb(255, 255, 255)"><span dir="ltr" style="margin:0px"><u class="x_ContentPasted0"><br>
</u></span></div>
Registration and Attendance</u></span>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px">
<br>
</p>
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px">
Further, at least one author of each accepted workshop paper has to register for the main conference. Workshop attendance is only granted for registered participants.</p>
</div>
<div class="x_elementToProof" style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="margin:0px"><u class="x_ContentPasted0"><br>
</u></span></div>
<div class="x_elementToProof" style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)">
<span dir="ltr" style="margin:0px"><u class="x_ContentPasted0">Workshop Organizers</u></span></div>
<div style="font-size:14px;font-family:Roboto, RobotoDraft, Helvetica, Arial, sans-serif;margin:0px;color:rgba(0, 0, 0, 0.87);background-color:rgb(255, 255, 255)">
<ul>
<li dir="ltr">
<p dir="ltr" class="x_ContentPasted0 x_elementToProof" style="margin-top:0px;margin-bottom:0px">
<span style="font-weight:700;margin:0px"><a href="https://www.linkedin.com/in/sudarshanlamkhede/" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" class="x_ContentPasted0" data-loopstyle="link" data-safelink="true" data-linkindex="3" style="margin:0px;color:rgb(41, 98, 255)">Sudarshan
Lamkhede</a></span> - Manager, Machine Learning - Search and Recommendations, Netflix Research. </p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px"><span style="font-weight:700;margin:0px"><a href="https://www.linkedin.com/in/praveenchandar/" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" class="x_ContentPasted0" data-safelink="true" data-linkindex="4" style="margin:0px;color:rgb(41, 98, 255)">Praveen
Chandar</a></span> - Staff Research Scientist, Spotify </p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px"><span style="font-weight:700;margin:0px"><a href="https://www.linkedin.com/in/vladan-radosavljevic-69244265/" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" class="x_ContentPasted0" data-safelink="true" data-linkindex="5" style="margin:0px;color:rgb(41, 98, 255)">Vladan
Radosavljevic</a></span> - Machine Learning Engineering Manager, Spotify </p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px"><span style="font-weight:700;margin:0px"><a href="https://www.linkedin.com/in/amitgoyal18/" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" class="x_ContentPasted0" data-safelink="true" data-linkindex="6" style="margin:0px;color:rgb(41, 98, 255)">Amit
Goyal</a></span> - Senior Applied Scientist, Amazon Music</p>
</li><li dir="ltr">
<p dir="ltr" class="x_ContentPasted0" style="margin-top:0px;margin-bottom:0px"><span style="font-weight:700;margin:0px"><a href="https://www.linkedin.com/in/lan-luo-b637a546/" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" class="x_ContentPasted0" data-safelink="true" data-linkindex="7" style="margin:0px;color:rgb(41, 98, 255)">Lan
Luo</a></span> - Associate Professor of Marketing, University of Southern California</p>
</li></ul>
<p dir="ltr" class="x_ContentPasted0 elementToProof" style="margin-top:0px;margin-bottom:0px">
If you have any questions please do not hesitate to reach out to the workshop organizers via <em class="x_ContentPasted0">organizers-ml4sm at googlegroups dot com</em></p>
</div>
</div>
</body>
</html>