Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

A. Rieger; Mariët Theune; N. Tintarev

doi:10.5281/zenodo.5883476

Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

A. Rieger, Mariët Theune, N. Tintarev

Web Information Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-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 language	English
Title of host publication	Proceedings of the 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2020)
Pages	50-54
DOIs	https://doi.org/10.5281/zenodo.5883476
Publication status	Published - 2021
Event	2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence - Dublin, Ireland Duration: 18 Dec 2020 → … Conference number: 2

Workshop

Workshop	2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence
Abbreviated title	NL4XAI 2020
Country/Territory	Ireland
City	Dublin
Period	18/12/20 → …

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10.5281/zenodo.5883476Licence: CC BY

2020.nl4xai-1.11Final published version, 114 KBLicence: CC BY

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title = "Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems",

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.",

author = "A. Rieger and Mari{\"e}t Theune and N. Tintarev",

year = "2021",

doi = "10.5281/zenodo.5883476",

language = "English",

pages = "50--54",

booktitle = "Proceedings of the 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2020)",

note = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, NL4XAI 2020 ; Conference date: 18-12-2020",

}

Rieger, A, Theune, M & Tintarev, N 2021, Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems. in Proceedings of the 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2020). pp. 50-54, 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, Dublin, Ireland, 18/12/20. https://doi.org/10.5281/zenodo.5883476

Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems. / Rieger, A.; Theune, Mariët; Tintarev, N.
Proceedings of the 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2020). 2021. p. 50-54.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Tintarev, N.

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BT - Proceedings of the 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2020)

T2 - 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence

Y2 - 18 December 2020

ER -

Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

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