Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity Bias

ZDF is a Public Service Media (PSM) broadcaster in Germany that uses recommender systems on its streaming service platform ZDFmediathek. One of the main use cases within the ZDFmediathek is Next Video, which is currently based on a Self-Attention based Sequential Recommendation model (SASRec). For this use case, we modified the loss function, the sampling method of negative items, and introduced the top-k negative sampling strategy and compared this to the vanilla SASRec model. We show that this not only reduces popularity bias, but also increases clicks and viewing volume compared to that of the vanilla version.