-------- Original-Nachricht -------- Betreff: [WI] CFP: UMUAI Special Issue on User Acceptance of Recommender Systems Datum: Wed, 9 Oct 2013 17:07:21 +0200 Von: Dietmar Jannach dietmar.jannach@tu-dortmund.de An: dietmar.jannach@cs.uni-dortmund.de

******************************************************************** CALL FOR PAPERS

Special Issue on User Acceptance of Recommender Systems

User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI)

*** Extended abstract submission deadline: February 1, 2014 *** Paper submission deadline (for accepted abstracts): March 31, 2014

UMUAI Web site: http://www.umuai.org/ *********************************************************************

SCOPE

The personalized recommendation of additional items of interest has become a standard functionality on many modern e-commerce sites and social web platforms. In parallel, the field of recommender systems (RS) has emerged as a research discipline of its own during the last two decades. Both in industry and academia, the main questions are "Which goals should a recommender system pursue in order to be effective?" and "What should we recommend to a user at a given point in time, and how should the interaction with the user look like in order to achieve these goals?"

Research in recommender systems is related to a number of other disciplines, including information retrieval, human-computer interaction, and machine learning. It also touches aspects of human decision making, consumer psychology and marketing. Correspondingly, there are various ways in which researchers try to assess the effectiveness, quality and ultimately the user acceptance of a recommender system through user studies or offline experiments.

Historically, measures that assess the accuracy of individual recommendation algorithms based on log data have dominated the research field. Over the years, however, a number of additional factors have been identified, which can have an influence on the user's perceived quality of a recommender system including the diversity or serendipity of recommendations but also how recommendations are presented to users.

In the last few years, increased interest in these topics can be observed in the research community, which resulted in a number of research papers, dedicated workshops or comprehensive frameworks and alternative approaches for user-centered system evaluation. The special issue focuses on research that deals with user-centered quality factors. It will comprise a collection of mature works and in-depth studies that are based on the developments and insights resulting from these recent research efforts.

The topics of interest include both reports on novel approaches, and studies assessing users' perception of recommender systems as well as methodological questions:

* Comparative analyses of potential quality factors impacting the acceptance of recommenders including perceived prediction accuracy, utility, diversity, novelty, serendipity, or familiarity of recommendations, as well as decision effort, trust, privacy or popularity aspects; * The role of user personality and other psychological factors for recommender systems; * Studies on the impact of recommender systems on decision making; * Explanations and other persuasive aspects of recommender systems; * Design guidelines and UI aspects for user acceptance of recommender systems; * Novel experimental approaches and frameworks for user-centered evaluation of RS including implicit user feedback measures such as eye-tracking and other sensor-based techniques; * Methodological questions for the user-centered evaluation of RS, including study design, methods for statistical analysis and reproducibility; * Predicting RS success and acceptance based on offline experimental designs and multi-metric studies; * Real-world studies of recommender systems' perception and acceptance; * Business- and marketing-oriented perspectives and success factors; * Theoretical models explaining RS impact including social influence models;

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 February 1, 2014. Also send a completed UMUAI self-assessment form that can be found at http://www.umuai.org/self-assessment.html

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 *** February 10, 2014 ***. 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 31, 2014 ***. The formatting guidelines and submission instructions for full papers can be found at http://www.umuai.org/paper_submission.html. Papers should not exceed 40 pages in journal format. Each paper submission should note that it is intended for the Special Issue on User Acceptance of Recommender Systems and be submitted via email to the address mentioned in the submission instructions above (submission@umuai.org).

The tentative timeline for the special issue is as follows: * February 1, 2014: Submission of extended abstracts * February 10, 2014: Notification regarding abstracts * March 31, 2014: Submission of full papers * June 30, 2014: First round review notifications * September 15, 2014: Revised papers due * November 15, 2014: Final notifications due * December 15, 2014: Camera ready papers due * February 15, 2015: Publication of special issue

GUEST EDITORS:

Dietmar Jannach, TU Dortmund, Germany dietmar.jannach@tu-dortmund.de

Markus Zanker, Alpen-Adria-Universität Klagenfurt, Austria markus.zanker@aau.at

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