ENHANCING SEQUENTIAL MUSIC RECOMMENDATION WITH PERSONALIZED POPULARITY AWARENESS

In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption. Nevertheless, traditional Transformer-based models, such as SASRec and BERT4Rec, while effective, encounter challenges due to the unique characteristics of music listening habits. In fact, existing models struggle to create a coherent listening experience due to rapidly evolving preferences. Moreover, music consumption is characterized by a prevalence of repeated listening, i.e. users frequently return to their favourite tracks, an important signal that could be framed as individual or personalized popularity. This work addresses these challenges by introducing a novel approach that incorporates personalized popularity information into sequential recommendation. By combining user-item popularity scores with model-generated scores, our method effectively balances the exploration of new music with the satisfaction of user preferences. Experimental results demonstrate that Personalized Most Popular recommender, a method solely based on user-specific popularity, outperforms existing state-of-the-art models and our proposed S6Rec, a model leveraging structured state space architecture for sequential recommendation. Furthermore, augmenting Transformer-based models and S6Rec with personalized popularity awareness yields superior performance, showing improvements ranging from 24.2% to 69.8%. Notably, S6Rec achieves similar performance to SASRec and BERT4Rec models, with convergence that is often faster or among the fastest. In summary, this work contributes by enhancing sequential music recommendation systems with personalized popularity awareness and introducing S6Rec, a model that competes effectively with Transformer-based approaches in terms of both performance and efficiency.