for the 6th Workshop on


Held in conjunction with ACM RecSys'14 in October in Foster City, Silicon
Valley, CA
* http://ls13-www.cs.uni-dortmund.de/homepage/rsweb2014/

Paper submission deadline: 
*** 21 July, 2014 *** 

The exponential growth of the Social Web poses challenges and new
opportunities for recommender systems. The Social Web has turned information
consumers into active contributors creating massive amounts of information.
Finding relevant and interesting content at the right time and in the right
context is challenging for existing recommender approaches. At the same
time, social systems by their definition encourage interaction between users
and both online content and other users, thus generating new sources of
knowledge for recommender systems. Web 2.0 users explicitly provide personal
information and implicitly express preferences through their interactions
with others and the system (e.g. commenting, friending, rating, etc.). These
various new sources of knowledge can be leveraged to improve recommendation
techniques and develop new strategies which focus on social recommendation.
The Social Web provides huge opportunities for recommender technology and in
turn recommender technologies can play a part in fuelling the success of the
Social Web phenomenon.

The goal of this workshop is to bring together researcher and practitioners
to explore, discuss, and understand challenges and new opportunities for
recommender systems and the Social Web.

We solicit original contributions in the following areas:

* Social network and folksonomy development: Recommending friends, tags,
bookmarks, blogs, music, communities etc.
* Leveraging models of user behavior on the Social Web for recommendation
* Recommender systems mash-ups, intelligent user interfaces, rich media
recommender systems
* Collaborative knowledge authoring, collective intelligence
* Topic emergence and evolution on the Social Web and their role in
recommendation process
* Recommender applications involving users or groups directly in the
recommendation process
* Exploiting folksonomies, social network information, user interactions,
and communities in the recommendation process
* The role of context in Social Web recommendation
* Trust and reputation aware social recommendations
* Semantic Web recommender systems, use of ontologies or microformats
* Empirical evaluation of social recommender techniques, success and failure
* Case studies and novel fielded social recommender applications
* Economy of community-based systems: Using recommenders to encourage users
to contribute and sustain participation
* Social recommender systems in the enterprise
* Recommendation for groups
* Processing big Social Web data volumes for recommendation, scalability

We also encourage submissions which relate research results from other areas
to the workshop topics.

* Dietmar Jannach, Department of Computer Science, TU Dortmund, Germany
* Jill Freyne, CSIRO ICT Center, Australia
* Werner Geyer, IBM Research, Cambridge, MA, USA
* Ido Guy, IBM Research, Haifa, Israel
* Andreas Hotho, Universität Würzburg, Germany
* Bamshad Mobasher, School of Computing, DePaul University, USA

We solicit short and long papers as well as research demos on all aspects of
recommender systems in the Social Web. Papers should be formatted according
to the style guide of RecSys'14.

Long papers present original research work and can be of up to 8 pages in
length. Short papers report on work in progress and can have up to 4 pages.
Presenters of demo systems are asked to submit short papers describing their

Papers should be submitted in PDF format through the EasyChair system at
Paper selection will be based on a peer review process; author names and
affiliations should be included in the paper.

At least one author of each accepted paper must register for the workshop. 

Information about registration is provided at the RecSys 2014 Web page: 

21 July, 2014     : Paper submission 
21 August, 2014   : Author notification
3 September, 2014 : Camera-ready papers
October 2014      : Workshop held

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