- From: Marco Polignano <marco.polignano@uniba.it>
- Date: Wed, 2 Aug 2023 18:04:35 +0200
- To: semantic-web@w3.org
- Message-ID: <9d663530-be2b-b1c8-835c-5c7461837301@uniba.it>
*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 *As a consequence of numerous requests, we have decided to extend our deadlines!* ******** DEADLINE EXTENDED ******** - *Abstract submission deadline*: August 7th, 2023 - *Paper submission deadline*: August 11th, 2023 - *Author notification*: August 27th, 2023 - *Camera-ready version deadline*: September 10th, 2023 *Submission site* --------------- _https://easychair.org/conferences/?conf=intrs23 <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: _https://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 <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
Received on Wednesday, 2 August 2023 16:04:51 UTC