Workshop on Human Decision Making in Recommender Systems (Decisions)

Interacting with a recommender system means to take different decisions such as selecting a song/movie from a recommendation list, selecting specific feature values (e.g., camera’s size, zoom) as criteria, selecting feedback features to be critiqued in a critiquing based recommendation session, or selecting a repair proposal for inconsistent user preferences when interacting with a knowledge-based recommender. In all these scenarios, users have to solve a decision task.

The complexity of decision tasks, limited cognitive resources of users, and the tendency to keep the overall decision effort as low as possible leads to the phenomenon of bounded rationality, i.e., users are exploiting decision heuristics rather than trying to take an optimal decision. Furthermore, preferences of users will likely change throughout a recommendation sessions, i.e., preferences are constructed in a specific decision environment and users do not know their preferences beforehand.

Decision making under bounded rationality is a door opener for different types of non-conscious influences on the decision behavior of a user. Theories from decision psychology and cognitive psychology are trying to explain these influences, for example, decoy effects and defaults can trigger significant shifts in item selection probabilities; in group decision scenarios, the visibility of the preferences of other group members can have a significant impact on the final group decision.

The major goal of this workshop was to establish a platform for industry and academia to present and discuss new ideas and research results that are related to the topic of human decision making in recommender systems.

  • Alexander Felfernig, Graz University of Technology
  • Li Chen, Hong Kong Baptist University
  • Monika Mandl, Graz University of Technology
Workshop Date

October 27, 2011

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