Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
Pavan Seshadri, Shahrzad Shashaani, Peter Knees · 2024
Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation—continuously providing new items within a single session in a contextually coherent manner—has been an emerging topic in current literature. User feedback—a positive or negative response to the item presented—is used to drive content recommendations by learning user preferences. We extend this idea to session-based recommendation to provide context-coherent music recommendations by modelling negative user feedback, i.e., skips, in the loss function.