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<p>Recommendation algorithms trained on a training set containing sub-optimal decisions may increase the likelihood of making more bad decisions in the future. We call this harmful effect self-induced bias, to emphasize that the bias is driven directly by the user’s past choices. In order to better understand the nature of self-induced bias of recommendation algorithms that are used by older adults with cognitive limitations, we have used agent-based simulation. Based on state-of-the-art results in psychology of aging and cognitive science, as well as our own empirical results, we have developed a cognitive model of an e-commerce client that incorporates cognitive decision-making abilities. We have evaluated the magnitude of self-induced bias by comparing results achieved by simulated agents with and without cognitive limitations due to age. We have also proposed new recommendation algorithms designed to counteract self-induced bias. The algorithms take into account user preferences and cognitive abilities relevant to decision making. To evaluate the algorithms, we have introduced 3 benchmarks: a simple product filtering method and two types of widely used recommendation algorithms: Content-Based and Collaborative filtering. Results indicate that the new algorithms outperform benchmarks both in terms of increasing the utility of simulated agents (both old and young), and in reducing self-induced bias.</p>
</abstract>

Recommendation algorithms trained on a training set containing sub-optimal decisions may increase the likelihood of making more bad decisions in the future. We call this harmful effect self-induced bias, to emphasize that the bias is driven directly by the user’s past choices. In order to better understand the nature of self-induced bias of recommendation algorithms that are used by older adults with cognitive limitations, we have used agent-based simulation. Based on state-of-the-art results in psychology of aging and cognitive science, as well as our own empirical results, we have developed a cognitive model of an e-commerce client that incorporates cognitive decision-making abilities. We have evaluated the magnitude of self-induced bias by comparing results achieved by simulated agents with and without cognitive limitations due to age. We have also proposed new recommendation algorithms designed to counteract self-induced bias. The algorithms take into account user preferences and cognitive abilities relevant to decision making. To evaluate the algorithms, we have introduced 3 benchmarks: a simple product filtering method and two types of widely used recommendation algorithms: Content-Based and Collaborative filtering. Results indicate that the new algorithms outperform benchmarks both in terms of increasing the utility of simulated agents (both old and young), and in reducing self-induced bias.

Using Cognitive Models to Understand and Counteract the Effect of Self-Induced Bias on Recommendation Algorithms

SWPS University of Social Sciences and Humanities

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Recommendation algorithms trained on a training set containing sub-optimal decisions may increase the likelihood of making more bad decisions in the future. We...

Using Cognitive Models to Understand and Counteract the Effect of Self-Induced Bias on Recommendation Algorithms

Online veröffentlicht: 11. März 2023

Seitenbereich: 73 - 94

Eingereicht: 23. Aug. 2022

Akzeptiert: 14. Feb. 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0008

Schlüsselwörter
recommender systems, cognitive limitations, aging, e-commerce

© 2023 Justyna Pawłowska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Using Cognitive Models to Understand and Counteract the Effect of Self-Induced Bias on Recommendation Algorithms

Online veröffentlicht: 11. März 2023

Seitenbereich: 73 - 94

Eingereicht: 23. Aug. 2022

Akzeptiert: 14. Feb. 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0008

Schlüsselwörterrecommender systems, cognitive limitations, aging, e-commerce

© 2023 Justyna Pawłowska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
recommender systems, cognitive limitations, aging, e-commerce