Enhancing Sequential Music Recommendation with Personalized Popularity Awareness

TL;DR

This paper introduces a novel method that enhances sequential music recommendation by integrating personalized popularity signals, significantly improving performance over existing models and addressing challenges in capturing evolving user preferences.

Contribution

It proposes a personalized popularity-aware approach that combines user-specific popularity with Transformer models, leading to substantial performance improvements in music recommendation.

Findings

Personalized popularity-based recommender outperforms state-of-the-art models.

Augmenting Transformers with popularity awareness improves accuracy by up to 69.8%.

The method effectively balances exploring new music and satisfying user preferences.

Abstract

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.