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User Modeling and User-Adapted Interaction (UMUAI)
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CALL FOR PAPERS
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Special Issue on
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RECOMMENDER SYSTEMS BASED ON RICH ITEM DESCRIPTIONS
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Abstracts due: September 15, 2017
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Papers due: December 1, 2017
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<a class="moz-txt-link-freetext"
href="https://tinyurl.com/umuai-cb-recsys-special-issue">https://tinyurl.com/umuai-cb-recsys-special-issue</a>
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BACKGROUND AND SCOPE
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Automated recommendations have become a pervasive feature of our
online user experience. Historically, the two main approaches of
building recommender systems are collaborative filtering (CF) and
content-based filtering (CB). In more recent years, this dichotomy
has become more and more blurred, and we observe various attempts to
incorporate additional side information and external knowledge
sources into the recommendation process, regardless of the adopted
recommendation approach. This side information predominantly
contains additional knowledge about the recommendable items, e.g.,
in terms of their features, metadata, category assignments,
relations to other items, user-provided tags and comments, or
related textual or multimedia content.
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The goal of the special issue is to highlight recent progress in the
area of recommender systems that propose novel approaches to
identify, extract, process, and leverage information about the items
in the recommendation process.
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TOPICS
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* Utilizing side information about items for user modeling and
recommending including structured sources, e.g., DBpedia, Linked
Open Data, BabelNet, Wikidata; textual sources, e.g., Wikipedia or
User-Generated Content like tags, reviews, and comments; and
multimedia ("low-level") features, e.g., videos or musical signals
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* Approaches that rely on a semantic (deep) understanding of items
and their features based, e.g., on formal ontologies
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* Applying deep learning methods to model item features
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* Leveraging rich item representations for more effective user
modeling and recommendation
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* Using side information about items to increase recommendation
quality in terms of novelty, diversity, or serendipity
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* Using side information about items to explain recommendations to
users
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* Leveraging side information and external sources for cross-lingual
recommendations
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* Using side information about items for transparent user modeling
compliant with the General Data Protection Regulation
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* Novel applications areas for recommender systems (e.g., music or
news recommendation, off-mainstream application areas) based on item
side information
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* User studies (e.g., on the user perception of recommendations),
field studies, in-depth experimental offline evaluations
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* Methodological aspects (evaluation protocols, metrics, and data
sets)
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PAPER SUBMISSION & REVIEW PROCESS
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Submissions will be pre-screened for topical fit based on extended
abstracts. Extended abstracts (up to three pages in journal format)
should be sent to <a class="moz-txt-link-abbreviated"
href="mailto:pasquale.lops@uniba.it">pasquale.lops@uniba.it</a>.
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Paper submission instructions can be found at
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<a class="moz-txt-link-freetext"
href="http://www.umuai.org/paper_submission.html">http://www.umuai.org/paper_submission.html</a>
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Timeline:
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* September 15, 2017 Abstract submission
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* December 1, 2017 Initial paper submission
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* March 15, 2018 Author notification
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* May 15, 2018 Revised versions due
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* July 20, 2018 Final notification
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* September 1, 2018 Camera-ready versions due
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* Spring 2019 Publication of special issue
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ABOUT THE JOURNAL
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User Modeling and User-Adapted Interaction (UMUAI) is a top-tier
international journal focusing on personalization research published
by Springer.
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- Journal rankings: <a class="moz-txt-link-freetext"
href="http://www.umuai.org/journal_rankings.html">http://www.umuai.org/journal_rankings.html</a>
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- Journal homepage: <a class="moz-txt-link-freetext"
href="http://www.umuai.org/">http://www.umuai.org/</a>
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GUEST EDITORS
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Pasquale Lops, University of Bari, Italy
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Dietmar Jannach, TU Dortmund, Germany
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Cataldo Musto, University of Bari, Italy
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Toine Bogers, Aalborg University Copenhagen, Denmark
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Marijn Koolen, Huygens/ING, Netherlands
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