[agents] UMUAI Special Issue on Recommender Systems based on Rich Item Descriptions

[Pasquale Lops]

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User Modeling and User-Adapted Interaction (UMUAI)
CALL FOR PAPERS

Special Issue on
RECOMMENDER SYSTEMS BASED ON RICH ITEM DESCRIPTIONS

Abstracts due: September 15, 2017
Papers due: December 1, 2017

https://tinyurl.com/umuai-cb-recsys-special-issue
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BACKGROUND AND SCOPE
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.

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.

TOPICS
* 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
* Approaches that rely on a semantic (deep) understanding of items and 
their features based, e.g., on formal ontologies
* Applying deep learning methods to model item features
* Leveraging rich item representations for more effective user modeling 
and recommendation
* Using side information about items to increase recommendation quality 
in terms of novelty, diversity, or serendipity
* Using side information about items to explain recommendations to users
* Leveraging side information and external sources for cross-lingual 
recommendations
* Using side information about items for transparent user modeling 
compliant with the General Data Protection Regulation
* Novel applications areas for recommender systems (e.g., music or news 
recommendation, off-mainstream application areas) based on item side 
information
* User studies (e.g., on the user perception of recommendations), field 
studies, in-depth experimental offline evaluations
* Methodological aspects (evaluation protocols, metrics, and data sets)

PAPER SUBMISSION & REVIEW PROCESS
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 pasquale.lops at uniba.it.

Paper submission instructions can be found at
http://www.umuai.org/paper_submission.html

Timeline:
* September 15, 2017    Abstract submission
* December 1, 2017        Initial paper submission
* March 15, 2018        Author notification
* May 15, 2018            Revised versions due
* July 20, 2018            Final notification
* September 1, 2018        Camera-ready versions due
* Spring 2019            Publication of special issue

ABOUT THE JOURNAL
User Modeling and User-Adapted Interaction (UMUAI) is a top-tier 
international journal focusing on personalization research published by 
Springer.
- Journal rankings: http://www.umuai.org/journal_rankings.html
- Journal homepage: http://www.umuai.org/

GUEST EDITORS
Pasquale Lops, University of Bari, Italy
Dietmar Jannach, TU Dortmund, Germany
Cataldo Musto, University of Bari, Italy
Toine Bogers, Aalborg University Copenhagen, Denmark
Marijn Koolen, Huygens/ING, Netherlands