CfP: RecSys'14 Workshop on New Trends in Content-based Recommender Systems - Sillicon Valley, CA, USA - 6-11 October 2014

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CALL FOR PAPERS

RecSys'14 Workshop on New Trends in Content-based Recommender Systems
(CBRecSys 2014)
http://ir.ii.uam.es/cbrecsys2014/

Sillicon Valley, CA, USA
6-11 October 2014
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Description & Objectives
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While content-based recommendation has been applied successfully in many
different domains, it has not seen the same level of attention as
collaborative filtering techniques have. In recent years, competitions like
the Netflix Prize, CAMRA, and the Yahoo! Music KDD Cup 2011 have spurred on
advances in collaborative filtering and how to utilize ratings and usage
data. However, there are many domains where content and metadata play a key
role, either in addition to or instead of ratings and implicit usage data.
For some domains, such as movies the relationship between content and usage
data has seen thorough investigation already, but for many other domains,
such as books, news, scientific articles, and Web pages we do not know if
and how these data sources should be combined to provide the best
recommendation performance.

The aim of the CBRecSys 2014 workshop is to bring together students,
faculty, researchers and professionals from both academia and industry who
are interested in addressing one or more aspects of content-based
recommendation. This would include both recommendation in domains where
textual content is abundant (e.g., books, news, scientific articles, jobs,
educational resources, Web pages, etc.) as well as dedicated comparisons of
content-based techniques with collaborative filtering in different domains.
Other relevant topics related to content-based recommendations could include
opinion mining for text/book recommendation, semantic recommendation,
content-based recommendation to alleviate cold-start problems, as well as
serendipity, diversity and cross-domain recommendation.

To facilitate exploration of these topics the workshop will feature an
in-workshop challenge on book recommendation. For this challenge a large
dataset containing user profiles with book ratings and tags and 2.8 million
book descriptions with library metadata, user ratings, tags, and reviews
from Amazon and LibraryThing will be made available. The rich textual nature
of the task makes the challenge an excellent venue to revisit the questions
of the benefits of content-based filtering vs. collaborative filtering and
metadata vs. ratings information.

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Topics of interest
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We invite original contributions in a variety of areas related to
content-based recommendation. Topics of interest include, but are not
limited to, the following:

* Processing text reviews
  - Estimating (implicit) ratings associated with text reviews
  - Opinion mining and sentiment analysis of text reviews to support
content-based recommendation
  - Extracting user personality traits and factors from text reviews for
recommendation
* Exploiting user generated contents
  - Social tag-based recommender systems
  - Mining microblogging data in content-based recommender systems
  - Exploiting Semantic Web and Linked Open Data in content-based
recommender systems
* Mining contextual data from content
  - Extraction of contextual signals from text contents for recommendation
  - Considering the time dimension in content-based recommendation
  - Mood- and sentiment-based recommender systems
* Addressing limitations of recommender system
  - Addressing the cold-start problem with content-based recommendation
approaches
  - Increasing diversity in content-based recommendations
  - Providing novelty in content-based recommendations
* Developing novel recommendation approaches
  - Hybrid strategies combining content-based and collaborative filtering
recommendations
  - Content-based approaches to cross-system and cross-domain recommendation
  - Latent factor models for content-based and hybrid recommendation

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Submissions
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We encourage two types of submissions to the workshop: (1) submissions
dedicated to one or more aspects of content-based recommendation, and (2)
submissions describing their participation in the book recommendation
challenge or that use the book recommendation data in an alternative manner.
We encourage submissions from diverse backgrounds and aim to promote the
exchange of ideas between researchers working in the abovementioned areas.

For both types of submissions, we welcome more mature ideas and approaches
as long papers (8 pages) and preliminary work as short papers (4 pages).

For full details on the submission format and procedure, please refer to the
Submissions page. Papers will be selected based on originality, quality, and
ability to promote discussion. Accepted papers will be included in the
workshop proceedings and published by CEUR. Extended versions of selected
workshop papers may be included in a special journal issue (TBD). At least
one author of each accepted paper must attend the workshop.

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Challenge
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To facilitate exploration of these topics the workshop will feature an
in-workshop challenge on book recommendation. For this challenge a large
dataset containing user profiles with book ratings and tags and 2.8 million
book descriptions with library metadata, user ratings, tags, and reviews
from Amazon and LibraryThing will be made available. The rich textual nature
of the task makes the challenge an excellent venue to revisit the questions
of the benefits of content-based filtering vs. collaborative filtering and
metadata vs. ratings information. Please consult the Challenge page for more
details about the setup of the challenge and how to obtain the data set.

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Important dates
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* First call for participation: May 5, 2014
* Challenge data set made available: May 12, 2014
* Paper submission deadline: July 21, 2014
* Challenge submission deadline: July 21, 2014
* Notification of acceptance: August 21, 2014
* Workshop & announcement of winners: RecSys 2014

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Program Committee (TBC)
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* Linas Baltrunas, Telefónica Research, Spain
* Alejandro Bellogín, Universidad Autónoma de Madrid, Spain
* Shlomo Berkovsky, NICTA, Australia
* Pablo Castells, Universidad Autónoma de Madrid, Spain
* Federica Cena, Universita' degli Studi di Torino, Italy
* Tommaso Di Noia, Politecnico di Bari, Italy
* Peter Dolog, Aalborg University, Denmark
* Juan M. Fernández-Luna, Universidad de Granada, Spain
* Ignacio Fernández-Tobías, Universidad Autónoma de Madrid, Spain
* Cristina Gena, Universita' degli Studi di Torino, Italy
* Juan F. Huete, Universidad de Granada, Spain
* Birger Larsen, Aalborg University, Denmark
* Pasquale Lops, University of Bari "Aldo Moro", Italy
* Alan Said, Centrum Wiskunde & Informatica, The Netherlands
* Markus Schedl, Johannes Kepler University, Austria
* Giovanni Semeraro, University of Bari "Aldo Moro", Italy
* Nava Tintarev, University of Aberdeen, UK
* David Vallet, Google, Australia

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Organizers
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* Toine Bogers (toine@hum.aau.dk), Aalborg University Copenhagen, Denmark
* Marijn Koolen (marijn.koolen@uva.nl), University of Amsterdam, the
Netherlands
* Iván Cantador (ivan.cantador@uam.es), Universidad Autónoma de Madrid,
Spain

For further questions please contact a member of the organizing committee.