This workshop is focusing on the aspect of integrating different theories of human decision making into the construction of recommender systems.

Topics of interest include but are not limited to:

  • Theories, algorithms and applications
    • Decision theories in recommender systems (e.g., priming, framing, and decoy effects)
    • Trust inspiring recommendation (e.g., explanation‐aware recommendation)
    • Persuasive recommendation (e.g., argumentation‐aware recommendation)
    • The role of emotions in recommender systems (e.g., emotion‐ware recommendation)
    • Mechanisms for effective group decision making (e.g., group recommendation heuristics)
    • Detection and avoidance of decision biases (e.g., in item presentations)
    • Sequential decision making and selection
    • Applications of the above mentioned features
  • User modeling and preference elicitation
    • Modelling user information search and decision making processes in recommender systems
    • Preference elicitation (e.g., eye tracking for automated preference detection)
    • Adaptive recommendation processes
    • Active approaches to preference elicitation
  • User interfaces
    • User interfaces for decision making (e.g., decision strategies and user ratings)
    • User interfaces for group decision making (e.g., group decision making in e‐tourism)
    • Explanations in Recommender Systems
  • Evaluation
    • User perceptions leading to the acceptance of recommendations
    • The role of diversity and serendipity for the acceptance of recommendations
    • Cultural differences (e.g., culture‐aware recommendation)
    • Empirical studies and innovative metrics of system performance