Recommender Systems and the Social Web

The exponential growth of the Social Web poses both challenges and new opportunities for recommender systems research. 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. Users of social media on the Web often explicitly provide personal information or implicitly express preferences through their interactions with other users or with resources (e.g. tagging, friending, rating, commenting, etc.). This Social Web therefore provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.

Organizers
  • Dietmar Jannach, Department of Computer Science, TU Dortmund, Germany
  • Jill Freyne, CSIRO ICT Center, Australia
  • Werner Geyer, IBM Research, Cambridge, USA
  • Ido Guy, IBM Research, Haifa, Israel
  • Andreas Hotho, Universit├Ąt of Wuerzburg, Germany
  • Bamshad Mobasher, School of Computing, DePaul University, USA
Workshop Date

Oct 6, 9.00 – 13.00

Location

Balboa Room

Web site

http://ls13-www.cs.uni-dortmund.de/homepage/rsweb2014

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