The Importance of Cognitive Biases in the Recommendation Ecosystem

• URL: http://arxiv.org/abs/2408.12492v2
• Date: Fri, 30 Aug 2024 07:58:46 GMT
• Title: The Importance of Cognitive Biases in the Recommendation Ecosystem
• Authors: Markus Schedl, Oleg Lesota, Stefan Brandl, Mohammad Lotfi, Gustavo Junior Escobedo Ticona, Shahed Masoudian,
• Abstract summary: We argue that cognitive biases also manifest in different parts of the recommendation ecosystem and at different stages of the recommendation process. We provide empirical evidence that biases such as feature-positive effect, Ikea effect, and cultural homophily can be observed in various components of the recommendation pipeline. We advocate for a prejudice-free consideration of cognitive biases to improve user and item models as well as recommendation algorithms.
• Score: 8.267786874280848
• License:
• Abstract: Cognitive biases have been studied in psychology, sociology, and behavioral economics for decades. Traditionally, they have been considered a negative human trait that leads to inferior decision-making, reinforcement of stereotypes, or can be exploited to manipulate consumers, respectively. We argue that cognitive biases also manifest in different parts of the recommendation ecosystem and at different stages of the recommendation process. More importantly, we contest this traditional detrimental perspective on cognitive biases and claim that certain cognitive biases can be beneficial when accounted for by recommender systems. Concretely, we provide empirical evidence that biases such as feature-positive effect, Ikea effect, and cultural homophily can be observed in various components of the recommendation pipeline, including input data (such as ratings or side information), recommendation algorithm or model (and consequently recommended items), and user interactions with the system. In three small experiments covering recruitment and entertainment domains, we study the pervasiveness of the aforementioned biases. We ultimately advocate for a prejudice-free consideration of cognitive biases to improve user and item models as well as recommendation algorithms.

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