[CFP] 10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’23)

*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=intrs2023_

*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=intrs2023_



*Registration *
------------
At least one author of each accepted paper needs to register and attend 
the workshop.


*Organizers *
----------
Peter Brusilovsky - peterb@pitt.edu <mailto:peterb@pitt.edu>
School of Information Sciences, University of Pittsburgh, USA

Marco de Gemmis - marco.degemmis@uniba.it <mailto:marco.degemmis@uniba.it>
Dept. of Computer Science, University of Bari Aldo Moro, Italy

Alexander Felfernig - alexander.felfernig@ist.tugraz.at 
<mailto:alexander.felfernig@ist.tugraz.at>
Institute for Software Technology, Graz University of Technology, Austria

Pasquale Lops - pasquale.lops@uniba.it <mailto:pasquale.lops@uniba.it>
Dept. of Computer Science, University of Bari Aldo Moro, Italy

Marco Polignano - marco.polignano@uniba.it <mailto:marco.polignano@uniba.it>
Dept. of Computer Science, University of Bari Aldo Moro, Italy

Giovanni Semeraro - giovanni.semeraro@uniba.it 
<mailto:giovanni.semeraro@uniba.it>
Dept. of Computer Science, University of Bari Aldo Moro, Italy

Martijn C. Willemsen - M.C.Willemsen@tue.nl <mailto:M.C.Willemsen@tue.nl>
Eindhoven University of Technology, The Netherlands