[Hcitaly] IntRS'21 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems - 2nd Cfp

Joint Workshop on Interfaces and Human Decision Making for Recommender Systems IntRS'21: https://intrs2021.wordpress.com/   Held in conjunction with the ACM Conference on Recommender Systems (RecSys 2021) Amsterdam, Netherlands, 25th September 2021. Full day. Submission deadline: Jul. 20th, 2021 Author notification: Aug. 20th, 2021 Camera-ready version: Aug. 31st, 2021 Submission site --------------------- https://easychair.org/conferences/?conf=intrs2021   Recommender systems are developed to help users in finding items that match their interests, needs, and preferences. Since the emergence of recommender systems, the majority of research in this area focused on improving predictive accuracy of recommendation. Much less attention has been paid to how users interact with the system and the efficacy of interface designs from users’ perspectives. The field has reached a point where it is necessary to look beyond algorithms, into users’ interactions, decision making processes, and overall end user experience. The IntRS workshop series focuses on the “human side” of recommender systems. Its goal is to integrate modern HCI approaches and theories of human decision making into the construction of recommender systems. It focuses particularly on the impact of interfaces on decision support and overall satisfaction. IntRS workshops have been previously held at RecSys 2014, 2015, 2016, 2017, 2018, 2019, and 2020. The aim of the IntRS’21 workshop is to bring together researchers and practitioners exploring the topics of designing and evaluating novel intelligent interfaces for recommender systems in order to: (1) share research and techniques, including new design technologies and evaluation methodologies, (2) identify next key challenges in the area, and (3) identify emerging topics. This workshop aims at establishing an interdisciplinary community with a focus on the interface design issues for recommender systems and promoting the collaboration opportunities between researchers and practitioners. We particularly encourage demos and mock-ups of systems to be used as a basis of a lively and interactive discussion in the workshop. In our opinion, the workshop will complement the technical aspects mainly discussed at the Conference with specific topics related to cognitive modeling and decision making. Topics of interests include, but are not limited to:   o Interaction, user modeling, and decision-making   - Cognitive Modeling for recommender systems   - Human-recommender interaction   - Decision theories and biases (e.g., priming, framing, and decoy effects)   - Detection and mitigation of decision biases (e.g., in item presentations)   - Preference detection (e.g., eye tracking for automated preference detection)   - Emotions and mood in recommender systems (e.g., emotion-aware recommendation)   - Personality in recommender systems (e.g., personality-aware recommendation)   - Trust inspiring recommender systems and interfaces (e.g., explanation-aware recommendation) - Controllability, transparency, and scrutability   - 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 o User Interfaces   - Visual interfaces for recommender systems   - Explanation interfaces for recommender systems   - 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)   o Evaluation   - Case studies   - Benchmarking platforms   - Empirical studies and evaluations of interfaces for recommender systems   - Empirical studies and evaluations of new interaction designs   - Evaluation methods and metrics (e.g., evaluation questionnaire design)   Submissions ----------------   Submission site: https://easychair.org/conferences/?conf=intrs2021   We encourage two types of submissions, which address novel interface issues in recommender systems:     - Short/Demo papers. The maximum length is 8 pages in the new single-column format.   - Long papers. The maximum length is 14 pages in the 8 pages in the new single-column format.   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.   Accepted papers will be included in workshop proceedings to be published on the CEUR-WS.org site. Note that at least one author of each accepted paper needs to register and attend the workshop. Organizers --------------   Peter Brusilovsky – peterb(a)pitt.edu School of Information Sciences, University of Pittsburgh, USA   Marco de Gemmis – marco.degemmis(a)uniba.it Dept. of Computer Science, University of Bari “Aldo Moro”, Italy   Alexander Felfernig – alexander.felfernig(a)ist.tugraz.at Institute for Software Technology, Graz University of Technology, Austria Elisabeth Lex - elisabeth.lex(a)tugraz.at Graz University of Technology, Austria Pasquale Lops – pasquale.lops(a)uniba.it Dept. of Computer Science, University of Bari “Aldo Moro”, Italy Giovanni Semeraro – giovanni.semeraro(a)uniba.it Dept. of Computer Science, University of Bari “Aldo Moro”, Italy Martijn C. Willemsen - M.C.Willemsen(a)tue.nl Eindhoven University of Technology, The Netherlands