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 paper 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 a Personalized Most Popular recommender, a method solely based on user-specific popularity, outperforms existing state-of-the-art models. Furthermore, augmenting Transformer-based models with personalized popularity awareness yields superior performance, showing improvements ranging from 25.2% to 69.8%. The code for this paper is available at https://github.com/sisinflab/personalized-popularity-awareness.

翻译：在音乐推荐领域，序列推荐系统已展现出捕捉音乐消费动态特性的潜力。然而，基于Transformer的传统模型（如SASRec和BERT4Rec）尽管有效，却因音乐收听习惯的独特性而面临挑战。事实上，由于用户偏好快速演变，现有模型难以构建连贯的收听体验。此外，音乐消费具有重复收听普遍性的特征，即用户频繁返回其喜爱的曲目，这一重要信号可被视作个体化或个性化的流行度。本文通过将个性化流行度信息融入序列推荐，提出一种新方法以应对这些挑战。通过将用户-项目流行度分数与模型生成分数相结合，我们的方法有效平衡了新音乐探索与用户偏好满足。实验结果表明，仅基于用户特定流行度的个性化最流行推荐器优于现有最先进模型。此外，为基于Transformer的模型增强个性化流行度感知可带来更优性能，提升幅度达25.2%至69.8%。本文代码发布于https://github.com/sisinflab/personalized-popularity-awareness。