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CHI-ANNOUNCEMENTS  December 2013, Week 2

CHI-ANNOUNCEMENTS December 2013, Week 2

Subject:

2nd CFP: UMUAI Special Issue on User Acceptance of Recommender Systems

From:

Dietmar Jannach <[log in to unmask]>

Reply-To:

Dietmar Jannach <[log in to unmask]>

Date:

Thu, 12 Dec 2013 20:42:47 +0100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (128 lines)

********************************************************************
2nd 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 ([log in to unmask]).

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
[log in to unmask]

Markus Zanker, Alpen-Adria-Universitšt Klagenfurt, Austria
[log in to unmask]

    ---------------------------------------------------------------
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