Evaluating Attribute Association Bias in Latent Factor Recommendation Models

This article explores how gender bias can become embedded within latent factor recommendation (LFR) models and how classifiers can reveal these implicit patterns. Using an industry case study focused on podcast recommendations, the authors demonstrate that even when gender attributes are removed, bias persists within the learned representations. Their evaluation framework identifies these issues and offers methods to measure attribute association bias, highlighting both strengths and limitations. The study calls for future research into non-binary bias evaluation, multi-group analysis, and public dataset replication to promote fairness and transparency in AI-driven recommendation systems.