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IntRS 2023: Interfaces & Human Decision Making for Recommender Systems

18-22 Sep 2023
Singapore, Singapore

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

IntRS'23: https://intrs2023.wordpress.com/
Held in conjunction with the ACM Conference on Recommender Systems (RecSys 
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


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 

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 

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

·      The role of emotions in recommender systems (e.g., emotion-aware 

·      Trust inspiring recommendation (e.g., explanation-aware recommendation)

·      Argumentation and persuasive recommendation (e.g., argumentation-aware 

·      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


·      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

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:

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 
 - Long papers. The maximum length is 16 pages (plus up to 2 pages of 

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:

At least one author of each accepted paper needs to register and attend the 

Peter Brusilovsky - peterb@pitt.edu
School of Information Sciences, University of Pittsburgh, USA

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

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

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

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

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

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


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