Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

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.