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<p class=MsoNormal>[Apologies if you receive this more than once]<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>============================================================<o:p></o:p></p>
<p class=MsoNormal>Final Call for Papers - paper submission deadline extended<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>International Workshop on Information Heterogeneity and
Fusion in Recommender Systems (HetRec 2010)<o:p></o:p></p>
<p class=MsoNormal>26 September 2010 | Barcelona, Spain <o:p></o:p></p>
<p class=MsoNormal>http://ir.ii.uam.es/hetrec2010/<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>In conjunction with the<o:p></o:p></p>
<p class=MsoNormal>4th ACM Conference on Recommender Systems (RecSys 2010) <o:p></o:p></p>
<p class=MsoNormal>http://recsys.acm.org/2010/ <o:p></o:p></p>
<p class=MsoNormal>============================================================
<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>+++++++++++++++<o:p></o:p></p>
<p class=MsoNormal>Important dates<o:p></o:p></p>
<p class=MsoNormal>+++++++++++++++<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal> * Paper
submission:
7 July 2010 (extended)<o:p></o:p></p>
<p class=MsoNormal> * Notification of paper
acceptance: 22 July 2010<o:p></o:p></p>
<p class=MsoNormal> * Camera-ready copies of accepted
papers: 30 July 2010<o:p></o:p></p>
<p class=MsoNormal> * HetRec 2010 Workshop:
26 September 2010<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>++++++++++<o:p></o:p></p>
<p class=MsoNormal>Motivation<o:p></o:p></p>
<p class=MsoNormal>++++++++++<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Recent years have shown much progress in the field of
recommender systems, including the development of innovative models and very
efficient algorithms. Almost all current systems are trying to make best use of
a single kind of data, and are designed for specific domains and applications,
without explicitly addressing the heterogeneity of the existing information.<o:p></o:p></p>
<p class=MsoNormal>As an example, some systems are based on analyzing user
ratings, while others concentrate on understanding purchase history.<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Recognizing this limitation, research attention has been
given to finding ways for combining/integrating/mediating user models for the purpose
of providing better personalized services to users in many information seeking
and ecommerce services. See for example the work done in the series of UbiqUM
workshops that traditionally takes place at conferences related to user
modelling, such as UMAP, IUI and ECAI. In spite of prior work, however, the
issue remained one of the major challenges for recommender systems.<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>The heterogeneity of personal information sources can be
identified in any of the three pillars of a recommendation algorithm: the
modelling of user preferences, the description of resource contents, and the
modelling and exploitation of the context in which recommendations are made.<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Increasingly, users create and manage more and more profiles
in online systems for different purposes, such as leisure (e.g., Facebook),
professional interests (e.g., LinkedIn), or specialized applications (e.g.,
LearnCentral for educational issues, PatientsLikeMe for health issues, etc.).
Similarly, rated, tagged or bookmarked resources belong to distinct<o:p></o:p></p>
<p class=MsoNormal>multimedia: text (e.g., del.icio.us, BibSonomy, Google
News), image (e.g., Flickr, Picasa), audio (e.g., Last.fm, Spotify), or video
(e.g., MovieLens, NetFlix, YouTube). Moreover, recommendation algorithms may
also present heterogeneity based on different types of input (e.g., explicit
feedback from ratings, reviews, tags, etc. vs. implicit feedback from records
of views, queries and purchases), or based on different levels of input
granularities (e.g., a user may not only rate individual songs, but also
albums, artists or even a full music genre).<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Finally, contextual factors also increase heterogeneity in
recommender systems. Location and time are key external elements that may
affect the relevance of the recommendations, as shown in recent works. Many other
factors can be taken into account as well, such as physical and social
environment, device and network settings, and external events, to name a few.
Approaches that integrate several of these factors into recommendation models
are needed.<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>HetRec workshop aims to attract the attention of students,
faculty and professionals both from academia and industry who are interested in
addressing and exploiting any of the above forms of information heterogeneity
and fusion in recommender systems. The work goals are broad.<o:p></o:p></p>
<p class=MsoNormal>First, we would like to raise awareness of the potential of
using multiple information sources. Then, we look for sharing expertise and
suitable models. Another dire need is for strong datasets, and one of our aims
is to establish benchmarks and standard datasets on which the problem would be
studied following the workshop. Our hope is that this workshop will put a basis
for a line of works, and will help shaping the research agenda. <o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>++++++++++++++++++<o:p></o:p></p>
<p class=MsoNormal>Topics of interest<o:p></o:p></p>
<p class=MsoNormal>++++++++++++++++++<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>The goal of the workshop is to bring together researchers
and practitioners interested in addressing the challenges posed by information
heterogeneity in recommender systems and studying information fusion in this
context. We aim at identifying the main challenges, suggesting and discussing
novel ideas for addressing these challenges, and proposing a research agenda
for future research at the domain.<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Topics of interest include, but are not limited to:<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Heterogeneity and fusion of information in user profiles<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal> * Fusion of user profiles from different
representations<o:p></o:p></p>
<p class=MsoNormal> * Combination of short- and long-term
user preferences<o:p></o:p></p>
<p class=MsoNormal> * Combination of different types of user
preferences: tastes, interests, needs, goals, mood, etc.<o:p></o:p></p>
<p class=MsoNormal> * Cross domain recommendations, based on
user preferences about different interest aspects (e.g., by merging movie and
music tastes)<o:p></o:p></p>
<p class=MsoNormal> * Cross representation recommendations,
considering diverse sources of user preferences: explicit and implicit feedback<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Heterogeneity and fusion of information in recommended
resources<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal> * Recommendation of resources of
different nature: news, reviews, scientific papers, etc.<o:p></o:p></p>
<p class=MsoNormal> * Recommendation of resources belonging
to different multimedia: text, image, audio, video<o:p></o:p></p>
<p class=MsoNormal> * Recommendation of resources annotated
in different languages<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Heterogeneity and fusion of information in contextual
features<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal> * Contextualisation of user preferences
(e.g., user preferences at work, on holidays, etc.)<o:p></o:p></p>
<p class=MsoNormal> * Cross context recommendations (e.g., by
merging information about location, time, social aspects, etc.)<o:p></o:p></p>
<p class=MsoNormal> * Multi-dimensional recommendation based
on several contextual features (e.g., physical and social environment, device
and network settings, external events, etc.)<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>++++++++++++++++++++<o:p></o:p></p>
<p class=MsoNormal>Organizing Committee<o:p></o:p></p>
<p class=MsoNormal>++++++++++++++++++++<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal> * Peter Brusilovsky, University of
Pittsburgh, USA<o:p></o:p></p>
<p class=MsoNormal> * Iv<span lang=HE dir=RTL
style='font-family:"Arial","sans-serif"'>á</span>n Cantador, Universidad Aut<span
lang=HE dir=RTL style='font-family:"Arial","sans-serif"'>ó</span>noma de
Madrid, Spain<o:p></o:p></p>
<p class=MsoNormal> * Yehuda Koren, Yahoo! Labs<o:p></o:p></p>
<p class=MsoNormal> * Tsvi Kuflik, University of Haifa,
Israel<o:p></o:p></p>
<p class=MsoNormal> * Markus Weimer, Yahoo! Labs<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>+++++++++++++++++++<o:p></o:p></p>
<p class=MsoNormal>Contact information<o:p></o:p></p>
<p class=MsoNormal>+++++++++++++++++++<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Contact e-mail: hetrec2010@easychair.org<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><b><i><span style='font-size:14.0pt;font-family:"Kunstler Script";
color:#1F497D'>Tsvika</span></i></b><span style='font-size:14.0pt;font-family:
"Kunstler Script";color:#1F497D'><o:p></o:p></span></p>
<p class=MsoNormal><span style='font-family:"Monotype Corsiva";color:#548DD4'> <o:p></o:p></span></p>
<p class=MsoNormal><i><span style='font-size:10.0pt;font-family:"Times New Roman","serif";
color:#1F497D'>Tsvi Kuflik, PhD.</span></i><span style='font-size:10.0pt;
font-family:"Times New Roman","serif";color:#1F497D'><o:p></o:p></span></p>
<p class=MsoNormal><i><span style='font-size:10.0pt;font-family:"Times New Roman","serif";
color:#1F497D'> Senior Lecturer</span></i><span style='font-size:10.0pt;
font-family:"Times New Roman","serif";color:#1F497D'><o:p></o:p></span></p>
<p class=MsoNormal><i><span style='font-size:10.0pt;font-family:"Times New Roman","serif";
color:#1F497D'> MIS department</span></i><span style='font-size:10.0pt;
font-family:"Times New Roman","serif";color:#1F497D'><o:p></o:p></span></p>
<p class=MsoNormal><i><span style='font-size:10.0pt;font-family:"Times New Roman","serif";
color:#1F497D'> The University of Haifa</span></i><span style='font-size:
10.0pt;font-family:"Times New Roman","serif";color:#1F497D'><o:p></o:p></span></p>
<p class=MsoNormal><i><span style='font-size:10.0pt;font-family:"Times New Roman","serif";
color:#1F497D'> Email: </span></i><span style='font-size:10.0pt;
font-family:"Times New Roman","serif";color:#1F497D'><a
href="mailto:tsvikak@is.haifa.ac.il"
title="blocked::mailto:tsvikak@is.haifa.ac.il"><i><span style='color:#1F497D;
text-decoration:none'>tsvikak@is.haifa.ac.il</span></i></a><o:p></o:p></span></p>
<p class=MsoNormal><i><span style='font-size:10.0pt;font-family:"Times New Roman","serif";
color:#1F497D'> Home page: </span></i><span style='font-size:10.0pt;
font-family:"Times New Roman","serif";color:#1F497D'><a
href="http://mis.hevra.haifa.ac.il/~tsvikak/Home.htm"
title="blocked::http://mis.hevra.haifa.ac.il/~tsvikak/Home.htm"><i><span
style='color:#1F497D;text-decoration:none'>http://mis.hevra.haifa.ac.il/~tsvikak/Home.htm</span></i></a><o:p></o:p></span></p>
<p class=MsoNormal><i><span style='font-size:10.0pt;font-family:"Times New Roman","serif";
color:#1F497D'> Tel: +972 4 8288511</span></i><span style='font-size:
10.0pt;font-family:"Times New Roman","serif";color:#1F497D'><o:p></o:p></span></p>
<p class=MsoNormal><i><span style='font-size:10.0pt;font-family:"Times New Roman","serif";
color:#1F497D'> Fax: +972 4 8288</span></i><span dir=RTL></span><i><span
lang=HE dir=RTL style='font-size:10.0pt;font-family:"Arial","sans-serif";
color:#1F497D'><span dir=RTL></span>283</span></i><o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
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