<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 id="divtagdefaultwrapper" style="font-size:12pt;color:#000000;font-family:Calibri,Helvetica,sans-serif;" dir="ltr">
<p style="margin-top:0;margin-bottom:0"></p>
<div> *** Call for Papers ***</div>
<div><br>
</div>
<div> 1st Workshop on Knowledge aware Recommender Systems - KaRS 2018</div>
<div> http://sisinflab.poliba.it/kars/2018/</div>
<div><br>
</div>
<div>co-located with 12th ACM Conference on Recommender Systems - RecSys 2018</div>
<div> https://recsys.acm.org/recsys18/</div>
<div><br>
</div>
<div><br>
</div>
<div>October 2-7, 2018 -- Vancouver, Canada</div>
<div><br>
</div>
<div>-- SCOPE --</div>
<div>We are pleased to invite you to contribute to the First Workshop on Knowledge-aware Recommender Systems held in conjunction with the ACM Conference on Recommender Systems (RecSys 2018), Vancouver, Canada, from October, 2nd to October 7th, 2018.</div>
<div><br>
</div>
<div>Over the last years we have been witnessing the advent of more and more precise and powerful recommendation algorithms and techniques able to effectively assess users’ tastes and predict information that would probably be of interest for them.</div>
<div>Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest of the recommendation
engine.</div>
<div>Although very effective in terms of accuracy of results, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move into the direction of providing novel and diverse results as well as generate an explanation
for the recommended items or support interactive and conversational recommendation processes.</div>
<div><br>
</div>
<div>This workshop will focus on all aspects related to the exploitation of external and explicit knowledge sources to feed a recommendation engine. The aim is to go beyond the traditional accuracy goal and to start a new generation of algorithms and approaches
which exploit the knowledge encoded in ontological and logic-based knowledge bases,knowledge graphs as well as the semantics emerging from the analysis of semi-structured textual sources.The aim of the workshop is to bring together researchers and practitioners
around the topics of designing and evaluating novel approaches for recommender systems in order to:</div>
<div>* share research and techniques, including new design technologies and evaluation methodologies,</div>
<div>* identify next key challenges in the area, and</div>
<div>* identify emerging topics.</div>
<div><br>
</div>
<div>This workshop aims at establishing an interdisciplinary community with a focus on the exploitation of (semi-)structured knowledge for recommender systems and promoting the collaboration opportunities between researchers and practitioners. We particularly
encourage demos and mock-ups of systems to be used as a basis of a lively and interactive discussion in the workshop.</div>
<div>Topics of interests include, but are not limited to:</div>
<div>* Knowledge-aware data models based on structured knowledge sources (e.g., Linked Open Data, BabelNet, Wikidata, etc.)</div>
<div>* Semantics-aware approaches exploiting the analysis of textual sources (e.g.,Wikipedia, Social Web, etc.)</div>
<div>* Deep learning methods to model semantic features</div>
<div>* Logic-based modeling of a recommendation process</div>
<div>* Knowledge representation and Automated Reasoning for recommendation engines</div>
<div>* Knowledge-based and conversational recommender systems</div>
<div>* Using knowledge-bases and knowledge-graphs to increase recommendation quality(e.g., in terms of novelty, diversity, serendipity, or explainability)</div>
<div>* Knowledge-aware explanations to recommendations (compliant with the General Data Protection Regulation)</div>
<div>* Using knowledge sources for cross-lingual recommendations</div>
<div>* Knowledge-aware user modeling</div>
<div>* Applications of knowledge-aware recommenders (e.g., music or news recommendation, off-mainstream application areas)</div>
<div>* User studies (e.g., on the user’s perception of knowledge-based recommendations),field studies, in-depth experimental offline evaluations</div>
<div>* Methodological aspects (evaluation protocols, metrics, and data sets)</div>
<div><br>
</div>
<div>-- PAPER SUBMISSION CATEGORIES --</div>
<div>We encourage the submission of original contributions, which address novel interface issues in recommender systems.</div>
<div><br>
</div>
<div>* LONG PAPERS. should report on substantial contributions of lasting value. The maximum length is 6 pages (plus up to 1 page references). Each accepted long paper will be included in the ACM on-line Workshop proceedings and presented in a plenary session
as part of the Workshop program.</div>
<div><br>
</div>
<div>* SHORT/DEMO PAPERS. typically discuss exciting new work that is not yet mature enough for a long paper. In particular, novel but significant proposals will be considered for acceptance to this category despite not having gone through sufficient experimental
validation or lacking strong theoretical foundation. Applications of recommender systems to novel areas are especially welcome. The maximum length is 3 pages (plus up to 1 page references). Each accepted short paper will be included in the ACM on-line Workshop
proceedings and presented in a poster session. The poster presentation may include a system demonstration. Selected short papers will be invited as oral presentations.</div>
<div><br>
</div>
<div>Submission page : https://easychair.org/conferences/?conf=kars2018 </div>
<div><br>
</div>
<div>-- PAPER FORMAT AND SUBMISSION --</div>
<div>All submissions and reviews will be handled electronically. KaRS 2018 submissions should be prepared according to the standard double-column ACM SIG proceedings format. Additional information about formatting and style files is available online. Papers
must be submitted to Easychair by 23:59, AoE (Anywhere on Earth) on July 16th, 2018. </div>
<div><br>
</div>
<div>Submitted work should be original. Simultaneous submissions to other conferences or journals is explicitly prohibited by ACM policies. However, technical reports or ArXiv disclosure prior to or simultaneous with KaRS submission, is allowed, provided they
are not peer-reviewed. Please refer to the ACM Publication License Agreement for further details.</div>
<div>Please take note that the official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of the conference. The official publication date affects the deadline
for any patent filings related to published work.</div>
<div>Submitted papers will be evaluated according to their originality, technical content, style, clarity, and relevance to the workshop. For short papers we will encourage also alternative modes of presentation such as demos, playing out of scenarios, mock-ups,
and alternate media such as video.</div>
<div>Demonstration sessions will provide the opportunity to show innovative interface designs for recommender systems.</div>
<div><br>
</div>
<div>-- IMPORTANT DATES --</div>
<div>* Paper submission deadline: July 16th, 2018</div>
<div>* Author notification: August 13th, 2018</div>
<div>* Camera-ready version deadline: August 27th, 2018</div>
<div><br>
</div>
<div>Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone.</div>
<div><br>
</div>
<div>-- ORGANIZING COMMITTEE --</div>
<div>* Vito Walter Anelli, Polytechnic University of Bari</div>
<div>* Tommaso Di Noia, Polytechnic University of Bari</div>
<div>* Pasquale Lops, University of Bari Aldo Moro</div>
<div>* Cataldo Musto, University of Bari Aldo Moro</div>
<div>* Markus Zanker, Free University of Bozen</div>
<div><br>
</div>
<div id="Signature">
<div id="divtagdefaultwrapper" dir="ltr" style="font-size:12pt; color:rgb(0,0,0); font-family:Calibri,Arial,Helvetica,sans-serif,"EmojiFont","Apple Color Emoji","Segoe UI Emoji",NotoColorEmoji,"Segoe UI Symbol","Android Emoji",EmojiSymbols">
<br>
</div>
</div>
</div>
<div><img alt="5x1000" src="http://www.poliba.it/sites/default/files/5X1000.png"></div>
</body>
</html>