Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models

• URL: http://arxiv.org/abs/2604.20847v1
• Date: Tue, 10 Feb 2026 15:24:41 GMT
• Title: Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
• Abstract summary: Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms.<n>We propose TASTE, a comprehensive dataset and benchmarking framework designed to highlight the role of multimodal information in music recommendation.<n>By leveraging recent large-scale self-supervised music encoders, we demonstrate the substantial value of the extracted audio representations across recommendation tasks.
• Score: 54.4270504928356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking stages, predominantly rely on collaborative filtering, which fails to exploit the intrinsic characteristics of audio and consequently leads to suboptimal performance, particularly in cold-start scenarios. However, existing music recommendation datasets often lack rich multimodal information, such as raw audio signals and descriptive textual metadata. Moreover, current recommender system evaluation frameworks remain inadequate, as they neither fully leverage multimodal information nor support a diverse range of algorithms, especially multimodal methods. To address these limitations, we propose TASTE, a comprehensive dataset and benchmarking framework designed to highlight the role of multimodal information in music recommendation. Our dataset integrates both audio and textual modalities. By leveraging recent large-scale self-supervised music encoders, we demonstrate the substantial value of the extracted audio representations across recommendation tasks, including candidate recall and CTR. In addition, we introduce the \textbf{MuQ-token} method, which enables more efficient integration of multi-layer audio features. This method consistently outperforms other feature integration techniques across various settings. Overall, our results not only validate the effectiveness of content-driven approaches but also provide a highly effective and reusable multimodal foundation for future research. Code is available at https://github.com/zreach/TASTE

Related papers

• From Classification to Recommendation: Empirical Analysis of Audio Embedding Models Application for Content-Based Music Recommendation [21.60252248417606]
  We evaluate six representative audio encoders across three types of music recommender systems.<n>We find that interaction-based sequential training substantially reduces performance differences among encoders.<n>We also find that increasing Semantic-ID capacity does not consistently improve generative recommender systems.
  arXiv  Detail & Related papers  (2026-08-07T08:03:29Z)
• AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation [111.61779149559645]
  We propose AudioX-Turbo, a framework for anything-to-audio generation that integrates varied multimodal conditions.<n>To support the training of AudioX-Turbo, we construct a large-scale, high-quality dataset, IF-caps-Pro, comprising approximately 9.2M samples.<n>We benchmark AudioX-Turbo across a wide range of tasks, finding that our model achieves superior performance, especially on text-to-audio and text-to-music generation.
  arXiv  Detail & Related papers  (2026-06-10T18:06:27Z)
• FIGMA: Towards FIne-Grained Music retrievAl [65.98380295254817]
  Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries.<n>We show that this limitation stems from the contrastive learning objective itself: despite being trained on long captions, CLAP-based models effectively utilize only the first few tokens.<n>We propose FIGMA, a multi-view contrastive architecture that addresses this limitation by jointly optimizing global audio-text alignment and frame-level, token-wise alignment.
  arXiv  Detail & Related papers  (2026-06-04T18:05:39Z)
• Multimodal Music Recommendation System using LLMs [53.1178230380919]
  We propose a framework for session-based music recommendation that enriches the LastFM-1K dataset with three complementary signals.<n>We adopt the E4SRec framework by extending it with multimodal features and different item ID encoder backbones.<n>Our experiments show that integrating content-based features improves over ID-only baselines up to 95% in terms of Recall and 79% in terms of NDCG.
  arXiv  Detail & Related papers  (2026-05-28T15:34:12Z)
• Audio Does Matter: Importance-Aware Multi-Granularity Fusion for Video Moment Retrieval [58.640807985155554]
  Video Moment Retrieval (VMR) aims to retrieve a specific moment semantically related to a given query.<n>Most existing VMR methods solely focus on the visual and textual modalities while neglecting the complementary but important audio modality.<n>We propose a novel Importance-aware Multi-Granularity fusion model (IMG), which learns to dynamically and selectively aggregate the audio-vision-text contexts for VMR.
  arXiv  Detail & Related papers  (2025-08-06T09:58:43Z)
• Learning Musical Representations for Music Performance Question Answering [10.912207282129753]
  multimodal learning methods are incapable of dealing with fundamental problems within the music performances.<n>Our primary backbone is designed to incorporate multimodal interactions within the context of music data.<n>Our experiments show state-of-the-art effects on the Music AVQA datasets.
  arXiv  Detail & Related papers  (2025-02-10T17:41:57Z)
• ATFLRec: A Multimodal Recommender System with Audio-Text Fusion and Low-Rank Adaptation via Instruction-Tuned Large Language Model [0.0]
  This study examines the integration of multimodal data text and audio into large language models (LLMs) Traditional text and audio recommenders encounter limitations such as the cold-start problem. Low-Rank Adaptation (LoRA) is introduced, which enhances efficiency without compromising performance.
  arXiv  Detail & Related papers  (2024-09-13T05:33:09Z)
• MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions [69.9122231800796]
  We present MMTrail, a large-scale multi-modality video-language dataset incorporating more than 20M trailer clips with visual captions.<n>We propose a systemic captioning framework, achieving various modality annotations with more than 27.1k hours of trailer videos.<n>Our dataset potentially paves the path for fine-grained large multimodal-language model training.
  arXiv  Detail & Related papers  (2024-07-30T16:43:24Z)
• LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation [49.89372182441713]
  We introduce LARP, a multi-modal cold-start playlist continuation model. Our framework uses increasing stages of task-specific abstraction: within-track (language-audio) contrastive loss, track-track contrastive loss, and track-playlist contrastive loss.
  arXiv  Detail & Related papers  (2024-06-20T14:02:15Z)
• AudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codes [6.375996974877916]
  We propose a method named AudioFormer, which learns audio feature representations through the acquisition of discrete acoustic codes. Our research outcomes demonstrate that AudioFormer attains significantly improved performance compared to prevailing monomodal audio classification models.
  arXiv  Detail & Related papers  (2023-08-14T15:47:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.