Mitigating Director Gender Bias in Movie Recommender Systems

• Studies in sociology and media studies have revealed a gender gap in the film industry, with an underrepresentation of female directors in film production.
• The implications of this disparity on recommendation systems are not widely researched.
• Most studies look at bias from a statistical perspective (e.g., popularity bias).
• Many content distribution platforms (like Netflix) utilize recommendation models for personalized user content.
• A recommender system filters information (e.g., user and item data) to provide personalized suggestions to users.
• We investigate whether this gender bias is embedded into various recommendation models, developed using different similarity metrics and algorithms.
• Our aim is to develop a fair movie recommender system that minimizes biases associated with the director’s gender.

Curious to learn more? Email me and I’ll forward you to the team! Or, check out their repo here