Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems
This addresses fairness concerns in recommendation systems, particularly in critical domains like job recommendations, but is incremental as it analyzes known factors without proposing a new solution.
The paper investigated factors associated with gender discrimination in collaborative recommender systems, finding that women receive less accurate recommendations than men based on anomalies in rating behavior, profile entropy, and profile size.
The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations. This varying degree of performance could impact users' trust in the system and may pose legal and ethical issues in domains where fairness and equity are critical concerns, like job recommendation. In this paper, we investigate several potential factors that could be associated with discriminatory performance of a recommendation algorithm for women versus men. We specifically study several characteristics of user profiles and analyze their possible associations with disparate behavior of the system towards different genders. These characteristics include the anomaly in rating behavior, the entropy of users' profiles, and the users' profile size. Our experimental results on a public dataset using four recommendation algorithms show that, based on all the three mentioned factors, women get less accurate recommendations than men indicating an unfair nature of recommendation algorithms across genders.