The Importance of Cognitive Biases in the Recommendation Ecosystem

TL;DR

This paper explores how cognitive biases are present in recommendation systems and argues that, contrary to traditional views, some biases can be beneficial if properly integrated into recommender models.

Contribution

It provides empirical evidence of specific cognitive biases in recommendation pipelines and advocates for their constructive consideration to enhance recommendation quality.

Findings

Biases like feature-positive effect, Ikea effect, and cultural homophily are observable in recommendation data and models.

Experiments show these biases influence user interactions and recommendations in recruitment and entertainment domains.

Considering cognitive biases can improve user and item modeling in recommender systems.

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.