Markus Schedl, Oleg Lesota, Shahed Masoudian,
"The Importance of Cognitive Biases in the Recommendation Ecosystem: Evidence of Feature-Positive Effect, Ikea Effect, and Cultural Homophily."
: Proceedings of the 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS @ RecSys 2024), Vol. 3815, 2024
Original Titel:
The Importance of Cognitive Biases in the Recommendation Ecosystem: Evidence of Feature-Positive Effect, Ikea Effect, and Cultural Homophily.
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS @ RecSys 2024)
Original Kurzfassung:
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. Lately, there has been growing interest in AI research to better understand the influence of such biases in classification, search, and also recommendation tasks. We argue that cognitive biases manifest in different parts of the recommendation ecosystem and 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. More importantly, we contest the 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 feature-positive effect, Ikea effect, and cultural homophily can be observed in the context of recommender systems, and discuss their potential for exploitation. 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.
Forschungs-