Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems
A. Rieger (TU Delft - Web Information Systems)
Mariët Theune (University of Twente)
N. Tintarev (Universiteit Maastricht, TU Delft - Web Information Systems)
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Abstract
Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices. One approach to mitigate such biases is through interface and interaction design. This survey reviews studies focused on cognitive bias mitigation of recommender system users during two processes: 1) item selection and 2) preference elicitation. It highlights a number of promising directions for Natural Language Generation research for mitigating cognitive bias including: the need for personalization, as well as for transparency and control.
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