通过用户个性化热门度提升音乐推荐连贯性，效果优于主流模型。
Enhancing Sequential Music Recommendation with Personalized Popularity Awareness
- 融合用户特定热门度与模型预测，平衡新曲探索与偏好满足。
- 纯基于用户热门度的推荐器超越现有顶尖模型。
- 在Transformer模型中加入个性化热门度，性能提升25.2%至69.8%。
在音乐推荐领域，序列推荐系统虽能捕捉音乐消费的动态特性，但传统基于Transformer的模型（如SASRec和BERT4Rec）因音乐听觉习惯的独特性面临挑战。现有模型难以构建连贯的听歌体验，且忽略用户频繁回听喜爱曲目的重要信号——即个体化热门度。本文提出一种新方法，将用户-项目个性化热门度与模型生成分数结合，有效平衡新音乐探索与用户偏好满足。实验表明，仅基于用户特定热门度的个性化最热门推荐器表现优于现有最先进模型；同时，在Transformer模型中引入个性化热门度感知，性能提升幅度达25.2%至69.8%。代码已开源。
原文摘要 · Abstract (English)
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.
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