Content filtering methods for music recommendation: A review

• URL: http://arxiv.org/abs/2507.02282v1
• Date: Thu, 03 Jul 2025 03:44:20 GMT
• Title: Content filtering methods for music recommendation: A review
• Authors: Terence Zeng, Abhishek K. Umrawal,
• Abstract summary: This review focuses on the role of content filtering in mitigating biases inherent in collaborative filtering approaches.<n>We explore various methods of song classification for content filtering, including lyrical analysis using Large Language Models (LLMs) and audio signal processing techniques.
• Score: 1.104960878651584
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recommendation systems have become essential in modern music streaming platforms, shaping how users discover and engage with songs. One common approach in recommendation systems is collaborative filtering, which suggests content based on the preferences of users with similar listening patterns to the target user. However, this method is less effective on media where interactions are sparse. Music is one such medium, since the average user of a music streaming service will never listen to the vast majority of tracks. Due to this sparsity, there are several challenges that have to be addressed with other methods. This review examines the current state of research in addressing these challenges, with an emphasis on the role of content filtering in mitigating biases inherent in collaborative filtering approaches. We explore various methods of song classification for content filtering, including lyrical analysis using Large Language Models (LLMs) and audio signal processing techniques. Additionally, we discuss the potential conflicts between these different analysis methods and propose avenues for resolving such discrepancies.

Related papers

• Separate This, and All of these Things Around It: Music Source Separation via Hyperellipsoidal Queries [53.30852012059025]
  Music source separation is an audio-to-audio retrieval task.<n>Recent work in music source separation has begun to challenge the fixed-stem paradigm.<n>We propose the use of hyperellipsoidal regions as queries to allow for an intuitive yet easily parametrizable approach to specifying both the target (location) and its spread.
  arXiv  Detail & Related papers  (2025-01-27T16:13:50Z)
• 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)
• Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
  We test for user strategization by conducting a lab experiment and survey. We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes" Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
  arXiv  Detail & Related papers  (2024-05-09T07:36:08Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery [56.77435520571752]
  We explore the intrinsic relationship between music discovery and popularity bias. We propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems. Our approach uses individual fairness to reflect a ground truth listening experience, i.e., if two songs sound similar, this similarity should be reflected in their representations.
  arXiv  Detail & Related papers  (2023-08-28T14:12:25Z)
• Video-to-Music Recommendation using Temporal Alignment of Segments [5.7235653928654235]
  We study cross-modal recommendation of music tracks to be used as soundtracks for videos. We build on a self-supervised system that learns a content association between music and video. We propose a novel approach to significantly improve the system's performance using structure-aware recommendation.
  arXiv  Detail & Related papers  (2023-06-12T15:40:31Z)
• Benchmarks and leaderboards for sound demixing tasks [44.99833362998488]
  We introduce two new benchmarks for the sound source separation tasks. We compare popular models for sound demixing, as well as their ensembles, on these benchmarks. We also develop a novel approach for audio separation, based on the ensembling of different models that are suited best for the particular stem.
  arXiv  Detail & Related papers  (2023-05-12T14:00:26Z)
• Follow the guides: disentangling human and algorithmic curation in online music consumption [1.4506962780822348]
  We analyze the complete listening history of about 9k users over one year. We show that the two types of recommendation offered by music platforms -- algorithmic and editorial -- may drive the consumption of more or less diverse content in opposite directions.
  arXiv  Detail & Related papers  (2021-09-08T20:14:48Z)
• Modurec: Recommender Systems with Feature and Time Modulation [50.51144496609274]
  We propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings.
  arXiv  Detail & Related papers  (2020-10-13T09:18:33Z)
• Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach [4.576379639081977]
  This paper studies the temporal information regarding when songs are played. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations.
  arXiv  Detail & Related papers  (2020-08-26T08:00:11Z)
• A session-based song recommendation approach involving user characterization along the play power-law distribution [0.0]
  This work is focused on two important issues that have not received much attention: managing gray-sheep users and obtaining implicit ratings. The first one is usually addressed by resorting to content information that is often difficult to obtain. The other drawback is related to the sparsity problem that arises when there are obstacles to gather explicit ratings.
  arXiv  Detail & Related papers  (2020-04-25T07:17:03Z)

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.