[AISWorld] [CFP] 10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’23)
Marco Polignano
marco.polignano at uniba.it
Tue Jul 4 05:23:07 EDT 2023
*10**th****Joint Workshop on Interfaces and Human Decision Making for
Recommender Systems (IntRS’23)*
*IntRS'23:* _https://intrs2023.wordpress.com/
<https://intrs2023.wordpress.com/>_
Held in conjunction with the ACM Conference on Recommender Systems
(RecSys 2023)
Singapore, 18th-22nd September 2023
- *Paper submission deadline*: August 3rd, 2023
- *Author notification*: August 27th, 2023
- *Camera-ready version deadline*: September 10th, 2023
*Submission site*
---------------
_https://easychair.org/conferences/?conf=intrs23_
*Recommender systems* are a popular kind of information access systems
that provides personalized recommendations to users based on their
preferences (i.e., current and past), behaviors, and feedback. These
systems are widely used in e-commerce, social networking, and content
sharing platforms, where the volume of available data and options can be
overwhelming for users. Recommender systems help users discover new
items, products, or content that match their interests and needs, and
enhance their overall experience by saving time, effort, and money.
Among the different aspects involved in the relationship between humans
and recommender systems, *User-centered design* plays a core role. This
approach involves users in many aspects of the design process to stress
trust, transparency, and efficacy as key factors for the successful
adoption and acceptance of recommender systems. In order to obtain these
results, the integration of recommender systems with interfaces designed
from users’ perspectives is crucial. *Interfaces that are
user-friendly*, intuitive, and visually appealing can facilitate the
interaction between users and recommender systems, and increase the
perceived usefulness and credibility of the recommendations. Moreover,
interfaces that allow users to provide feedback, adjust their
preferences, and control the level of personalization can increase their
engagement and satisfaction with the system. One of the key challenges
in designing interfaces for recommender systems is to balance the level
of personalization with the diversity and serendipity of the
recommendations. While users may expect the system to recommend items
that match their exact preferences and past behaviors, too much
personalization can lead to a narrow and repetitive experience that
limits their exploration and discovery of new options. Therefore,
interfaces that provide a variety of options, alternative
recommendations, and serendipitous discoveries can create a more
engaging and rewarding experience for users.
The IntRS workshop brings together an *interdisciplinary community of
researchers and practitioners *who share research on new recommender
systems (informed by psychology), including new design technologies and
evaluation methodologies, and aim to identify critical challenges and
emerging topics in the field. Indeed, the workshop focuses particularly
on the impact of interfaces on decision support and overall
satisfaction, and it is also connected to the topics of *Human-Centered
AI, Explainability of decision-making models, User-adaptive XAI
systems*, which are becoming more and more popular in the last years
especially in domains where recommended options might have ethical and
legal impacts on users. The integration of XAI with recommender systems
is crucial for enhancing their transparency, interpretability, and
accountability. XAI can help users understand why a particular
recommendation is made, what data and algorithms are used, and what
factors influence the outcome. This can increase the user’s trust and
confidence in the system, and improve their satisfaction and engagement
with the recommendations. The explanations should be presented in a way
that is understandable, concise, and relevant to the user’s context and
goals. This requires collaboration between XAI researchers, designers,
and end-users to ensure that the explanations meet the user’s
expectations and needs. An interesting research direction that has
recently received renewed interest is to investigate how users interact
with recommenders based upon their cognitive model of the system.
Previous work, investigated the impact of users’ mental models of
recommender systems on their interactions with them and drew a theory to
understand the key determinants motivating users to such user behavior.
*We believe that the paradigm that describes the relationship between
humans and recommender systems is changing and evolving from a
“human-centered” design approach toward a symbiotic vision.* From this
point of view, the mutual exchange of knowledge between human and system
will lead us towards “symbiotic recommender systems”, in which both
parties learn by observing each other. We hope IntRS will be the forum
where fresh ideas on this topic will be discussed.
Topics of interests include, but are not limited to:
*User Interfaces*
·Visual interfaces for recommender systems
·Explanation interfaces for recommender systems
·Ethical issues (Fairness and Biases) in explainable interfaces
·Collaborative multi-user interfaces (e.g., for group decision making)
·Spoken and natural language interfaces
·Trust-aware interfaces
·Social interfaces
·Context-aware interfaces
·Ubiquitous and mobile interfaces
·Conversational interfaces
·Example- and demonstration-based interfaces
·New approaches to designing interfaces for recommender systems
·User interfaces for decision making (e.g., decision strategies and user
ratings)
*Interaction, user modeling, and decision-making*
·Cognitive Modeling for recommender systems
·Symbiotic recommender systems
·Explainability of decision making models
·User-adaptive XAI systems
·Human-recommender interaction
·Controllability, transparency, and scrutability
·Decision theories and biases (e.g., priming, framing, and decoy effects)
·Detection and avoidance of decision biases (e.g., in item presentations)
·Preference elicitation and construction (e.g., eye tracking for
automated preference elicitation)
·The role of emotions in recommender systems (e.g., emotion-aware
recommendation)
·Trust inspiring recommendation (e.g., explanation-aware recommendation)
·Argumentation and persuasive recommendation (e.g., argumentation-aware
recommendation)
·Cultural differences (e.g., culture-aware recommendation)
·Mechanisms for effective group decision making (e.g., group
recommendation heuristics)
·Decision theories for effective group decision making (e.g., hidden
profile management)
·Voting Advice Applications
*Evaluation*
·Case studies
·Benchmarking platforms
·Empirical studies and evaluations of new interfaces
·Empirical studies and evaluations of new interaction designs
·Evaluation methods and metrics (e.g., evaluation questionnaire design)
*Paper Formatting Instructions and Submission *
--------------------------------------------
Accepted papers will be included in the workshop proceedings to be
published on the CEUR-WS.org site.
Therefore, we suggest to prepare the submissions according to the
CEUR-ART style for writing papers to be published with CEUR-WS.
Style files and templates are available online:
http://ceur-ws.org/Vol-XXX/CEURART.zip
<http://ceur-ws.org/Vol-XXX/CEURART.zip>
*The format adopted by IntRS '23 is: 1-column style. **
***
We encourage two types of submissions:
- *Short/Demo papers*. The maximum length is 8 pages (plus up to 2
pages of references).
* - Long papers. *The maximum length is 16 pages (plus up to 2 pages of
references).
Submitted papers will be evaluated according to their originality,
technical content, style, clarity, and relevance to the workshop.
For short papers we will encourage alternative modes of presentation
such as demos, playing out of scenarios, mockups, and alternate media
such as video.
Demonstration sessions will provide the opportunity to show innovative
interface designs for recommender systems.
Submission site:
_https://easychair.org/conferences/?conf=intrs23_
*Registration *
------------
At least one author of each accepted paper needs to register and attend
the workshop.
*Organizers *
----------
Peter Brusilovsky - peterb at pitt.edu <mailto:peterb at pitt.edu>
School of Information Sciences, University of Pittsburgh, USA
Marco de Gemmis - marco.degemmis at uniba.it <mailto:marco.degemmis at uniba.it>
Dept. of Computer Science, University of Bari Aldo Moro, Italy
Alexander Felfernig - alexander.felfernig at ist.tugraz.at
<mailto:alexander.felfernig at ist.tugraz.at>
Institute for Software Technology, Graz University of Technology, Austria
Pasquale Lops - pasquale.lops at uniba.it <mailto:pasquale.lops at uniba.it>
Dept. of Computer Science, University of Bari Aldo Moro, Italy
Marco Polignano - marco.polignano at uniba.it <mailto:marco.polignano at uniba.it>
Dept. of Computer Science, University of Bari Aldo Moro, Italy
Giovanni Semeraro - giovanni.semeraro at uniba.it
<mailto:giovanni.semeraro at uniba.it>
Dept. of Computer Science, University of Bari Aldo Moro, Italy
Martijn C. Willemsen - M.C.Willemsen at tue.nl <mailto:M.C.Willemsen at tue.nl>
Eindhoven University of Technology, The Netherlands
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