-------- Original-Nachricht -------- Betreff: [AISWorld] 2nd CFP: UMUAI Special Issue on Context-Aware Recommender Systems Datum: Wed, 23 Nov 2011 18:43:20 +0000 Von: dietmar.jannach@tu-dortmund.de An: aisworld@lists.aisnet.org
******************************************************************** 2nd CALL FOR PAPERS
Special Issue on Context-Aware Recommender Systems
User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI)
*** Extended abstract submission deadline: December 1, 2011 *** Paper submission deadline (for accepted abstracts): March 15, 2012
UMUAI Web site: http://www.umuai.org/ *********************************************************************
SCOPE
Recommender systems represent a popular area of personalization technologies, which has enjoyed a tremendous amount of research and development activity in both academia and industry in the last 10-15 years. Recommender systems research typically explores and develops techniques and applications for recommending various products or services to individual users based on the knowledge of users' tastes and preferences as well as users' past activities (such as previous purchases), which are applicable in a variety of domains and settings.
While a substantial amount of research has already been performed in the area of recommender systems, the vast majority of existing approaches has focused on recommending the most relevant items to users and does not take into account any additional contextual information, such as time, location, weather, or the company of other people. In other words, traditionally recommender systems deal with applications having only two types of entities, users and items, and do not put them into a context when providing recommendations. However, the importance of contextual information has been recognized by researchers and practitioners in many disciplines (such as e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management), and, in the past several years, context-awareness has been increasingly recognized as a critical issue in many recommendation applications and has been explored by a number of recommender systems researchers. This is evidenced by an increasing number of papers on context-aware recommender systems that appear in major conferences (such as ACM Conference on Recommender Systems) as well as the continued success of several events specifically dedicated to context-aware recommender systems (such as the annual Workshop on Context-Aware Recommender Systems in 2009, 2010, and 2011, and Challenge on Context-Aware Movie Recommendation in 2010 and 2011).
This UMUAI special issue on context-aware recommender systems builds on the recent activities in this area and is designed to solicit papers that report on recent significant advances, carry out innovative explorations, and establish foundations for further research. This special issue interprets the notion of context broadly, as any additional conditions and circumstances (i.e., beyond user profile and item content information) that may affect user preferences for items. Therefore, a wide range of specific usage contexts would be considered appropriate for this special issue, including location-aware or mobile recommendation, time-dependent recommendation, weather-aware recommendation, companion-aware or person-proximity-aware recommendation, interaction-aware recommendation, and so on. Note, however, that user profile information (e.g., user demographics or a set of previously rated items by a user) and item content information (e.g., item features) typically are not considered contextual information in recommender systems literature, but rather constitute standard, traditional inputs to recommendation algorithms for user-item preference estimation. The topics of interest for the special issue include (but are not limited to):
* Context modeling techniques for recommender systems; * Context-aware user modeling for recommender systems; * Acquisition, prediction, and mining of contextual information in recommender systems; * Algorithms for context-aware recommender systems; * Interacting with context-aware recommender systems; * Novel applications for context-aware recommender systems; * Large-scale context-aware recommender systems; * Context-aware recommendation to groups; * Evaluation and user studies of context-aware recommender systems; * Privacy issues in context-aware recommender systems.
PAPER SUBMISSION& REVIEW PROCESS
The prospective authors must first submit an extended abstract of no more than 4 single-spaced pages, formatted with 12 pt font and 1 inch margins, by email directly to the special issue editors by *** December 1, 2011 ***.
All submitted abstracts will receive an initial screening by the special issue editors. Authors of abstracts will be notified about the results of the initial screening by *** December 15, 2011 ***. Abstracts that do not pass this initial screening (i.e., the abstracts that are deemed not to have a reasonable chance of acceptance) will not be considered further.
Authors of abstracts that pass the initial screening will be invited to submit the full version of the paper by *** March 15, 2012 ***. The formatting guidelines and submission instructions for full papers can be found at http://www.umuai.org/paper_submission.html. The special issue papers should not exceed 40 pages in journal format. Each paper submission should note that it is intended for the Special Issue on Context-Aware Recommender Systems and be submitted via email to the address mentioned in the submission instructions above (submission@umuai.org).
The further tentative timeline for the special issue is as follows: * June 15, 2012: First round review notifications * September 15, 2012: Revisions of papers due * December 1, 2012: Final notifications due * January 1, 2013: Camera ready papers due * February 15, 2013: Publication of special issue
GUEST EDITORS:
Gediminas Adomavicius, University of Minnesota, USA gedas@umn.edu
Dietmar Jannach, TU Dortmund, Germany dietmar.jannach@tu-dortmund.de
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