Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

A. Rieger, Mariët Theune, N. Tintarev

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings of the 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2020)
Pages50-54
DOIs
Publication statusPublished - 2021
Event2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence - Dublin, Ireland
Duration: 18 Dec 2020 → …
Conference number: 2

Workshop

Workshop2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence
Abbreviated titleNL4XAI 2020
CountryIreland
CityDublin
Period18/12/20 → …

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