Human Interaction with Recommendation Systems
This addresses a fundamental issue in recommendation systems for developers and researchers, though it is incremental as it builds on existing feedback loop models.
The paper tackles the problem of recommendation systems creating feedback loops that bias user data, proving that naive estimators ignoring this dynamic are inconsistent and showing consistent estimators are efficient with myopic agents, validated through simulations.
Many recommendation algorithms rely on user data to generate recommendations. However, these recommendations also affect the data obtained from future users. This work aims to understand the effects of this dynamic interaction. We propose a simple model where users with heterogeneous preferences arrive over time. Based on this model, we prove that naive estimators, i.e. those which ignore this feedback loop, are not consistent. We show that consistent estimators are efficient in the presence of myopic agents. Our results are validated using extensive simulations.