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Call for Papers

Special issue on Recommenders on the Web
ACM Transactions on the Web

GUEST EDITORS

John Riedl
Barry Smyth

Recommender systems are changing the way people interact with the
Web. From e-commerce sites like Amazon.com to news and information
sites like digg and slashdot, recommenders help people choose between
diverse products and complex information, by providing a more
personalized information access experience. While much of the
published research on recommenders has focused on the algorithms that
power the recommendation process, many research challenges remain,
especially when it comes to the applications, interfaces and social
implications of recommenders.

This special issue of ACM Transactions on the Web aims to gather a
collection of high quality contributions that reflect recent
innovations in the field of Web-based recommender systems. Papers may
focus on novel Web interfaces for recommenders, on emerging
applications of recommenders, or on the ways recommenders fit into the
Social Web. Particular areas of interest include, but are not limited
to:

Applications of Recommenders on the Web

Recommenders on the Web, including the mobile Web and e-commerce;
recommenders and community, including group recommenders, recommenders
to support social networking, and techniques for leveraging the social
graph in forming recommendations.

Recommendation Interfaces

Novel recommendation interfaces, including Web, mobile, and Web2.0;
emerging interface technologies, including haptic interfaces, group
interfaces, and public-displays; the role of explanations in
recommender systems; evaluating recommendation interfaces through user
studies and other HCI approaches.

The Social Implications of Recommender Systems

User privacy in recommender systems; privacy-preserving recommendation
techniques; security and data protection; the robustness of
recommender systems (e.g., to recommendation spam); techniques for
detecting and coping with malicious users; on the role of trust in
recommender systems; computational models of trust for recommender
applications.

Recommender Algorithms for the Web

Novel algorithms especially suited to Web applications; evaluation
techniques for algorithms that effectively predict performance in
practice; hybrid collaborative and content-based recommenders;
conversational recommenders.

Guest Editor Contact Information

Professor John Riedl
Department of Computer Science and Engineering
University of Minnesota
Minneapolis, MN 55455
riedl@cs.umn.edu
http://www.cs.umn.edu/~riedl

Professor Barry Smyth
Digital Chair of Computer Science,
School of Computer Science and Informatics,
College of Engineering Mathematical and Physical Sciences,
University College Dublin, Belfield, Dublin 4, Ireland.
Barry Smyth <barry.smyth@ucd.ie>
Tel: +353-1-7162473 | Fax: +353-1-2697262
http://csiweb.ucd.ie/Staff/AcademicStaff/bsmyth/

Submission Information

Prospective authors, please submit your paper according to the
directions on the ACM TWEB Web site following the content and
formatting guidelines available at
http://www.acm.org/tweb/author.html. There you can also find detailed
information about the ACM TWEB review process. When submitting your
paper, please mention that it is to be considered for the special
issue on Recommenders on the Web. In addition, please send a copy of
your paper to <riedl@cs.umn.edu> and <barry.smyth@ucd.ie>, with the
Subject line "TWeb: Recommenders on the Web".

Papers due: September 15, 2008
Author notification: January 30, 2009
Revised versions of accepted papers due: March 16, 2009
        (all accepted papers expected to undergo a minor set of revisions)
Final materials for publication due: May 1, 2009
Special issue published: August 2009 (tentative)