Users interact with recommender systems to obtain useful information about product or services that may be of interest for them. But, while users are interacting with a recommender system to fulfill a primary task, which is usually the selection of one or more items, they are facing several other decision problems. For instance, they may be requested to select specific feature values (e.g., camera’s size, zoom) as criteria for a search, or they could have to select feedback features to be critiqued in a critiquing
based recommendation session, or they may need to select a repair proposal for inconsistent user preferences when interacting with a recommender. In all these scenarios, and in many others, users of recommender systems are facing decision tasks. The complexity of decision tasks, limited cognitive resources of users, and the tendency to keep the overall decision effort as low as possible is modeled by theories that conjecture “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 session, i.e., preferences are constructed in a specific decision environment and users may not fully know their preferences beforehand.
Theories from decision psychology and cognitive psychology have already elaborated a number of methodological tools for explaining and predicting the user behavior in these scenarios, but recommender systems hardly integrate this knowledge in the computational model.
The major goal of this workshop is 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.
We are specifically interested in the role of decision theories in advancing recommender systems research and applications.