Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

Conference paper (2021)

Authors

A. Rieger Web Information Systems - EEMCS
Mariët Theune University of Twente
N. Tintarev Web Information Systems - EEMCS , Universiteit Maastricht
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2021 A. Rieger, Mariët Theune, N. Tintarev
DOI: https://doi.org/10.5281/zenodo.5883476
More Info
expand_more

Published Date

2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information 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.

Files

Towardnatural_rieger2020.pdf
(pdf | 0.183 Mb)
Unknown license

Download not available

2020.nl4xai_1.11.pdf
(pdf | 0.196 Mb)
help_outline