Using cognitive models to understand and counteract the effect of self-induced bias on recommendation algorithms
Using cognitive models to understand and counteract the effect of self-induced bias on recommendation algorithms
StatusVoR
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Authors
Pawłowska, Justyna
Rydzewska, Klara
Wierzbicki, Adam
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Date
2023-03-11
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Journal of Artificial Intelligence and Soft Computing Research
Issue
2
Volume
13
Pages
Pages
73-94
ISSN
2083-2567
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Access date
2023-03-11
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Abstract EN
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.
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Keywords PL
Keywords EN
recommender systems
cognitive limitations
aging
e-commerce
cognitive limitations
aging
e-commerce
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Wspieranie ludzi w starszym wieku w dokonywaniu bardziej optymalnych wyborów w złożonych zadaniach decyzyjnych dotyczących zakupów internetowych
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