"Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation

• URL: http://arxiv.org/abs/2507.17356v2
• Date: Mon, 04 Aug 2025 14:20:55 GMT
• Title: "Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation
• Authors: Viet-Anh Tran, Bruno Sguerra, Gabriel Meseguer-Brocal, Lea Briand, Manuel Moussallam,
• Abstract summary: On music streaming services, listening sessions are often composed of a balance of familiar and new tracks.<n>We propose a model that leverages audio information to predict in advance the activation of new tracks.
• Score: 6.875744149600454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: On music streaming services, listening sessions are often composed of a balance of familiar and new tracks. Recently, sequential recommender systems have adopted cognitive-informed approaches, such as Adaptive Control of Thought-Rational (ACT-R), to successfully improve the prediction of the most relevant tracks for the next user session. However, one limitation of using a model inspired by human memory (or the past), is that it struggles to recommend new tracks that users have not previously listened to. To bridge this gap, here we propose a model that leverages audio information to predict in advance the ACT-R-like activation of new tracks and incorporates them into the recommendation scoring process. We demonstrate the empirical effectiveness of the proposed model using proprietary data, which we publicly release along with the model's source code to foster future research in this field.

Related papers

• POET: Prompt Offset Tuning for Continual Human Action Adaptation [61.63831623094721]
  We aim to provide users and developers with the capability to personalize their experience by adding new action classes to their device models continually.<n>We formalize this as privacy-aware few-shot continual action recognition.<n>We propose a novel-temporal learnable prompt tuning approach, and are the first to apply such prompt tuning to Graph Neural Networks.
  arXiv  Detail & Related papers  (2025-04-25T04:11:24Z)
• Active Human Feedback Collection via Neural Contextual Dueling Bandits [84.7608942821423]
  We propose Neural-ADB, an algorithm for collecting human preference feedback when the underlying latent reward function is non-linear.<n>We show that when preference feedback follows the Bradley-Terry-Luce model, the worst sub-optimality gap of the policy learned by Neural-ADB decreases at a sub-linear rate as the preference dataset increases.
  arXiv  Detail & Related papers  (2025-04-16T12:16:10Z)
• Slow Thinking for Sequential Recommendation [88.46598279655575]
  We present a novel slow thinking recommendation model, named STREAM-Rec.<n>Our approach is capable of analyzing historical user behavior, generating a multi-step, deliberative reasoning process, and delivering personalized recommendations.<n>In particular, we focus on two key challenges: (1) identifying the suitable reasoning patterns in recommender systems, and (2) exploring how to effectively stimulate the reasoning capabilities of traditional recommenders.
  arXiv  Detail & Related papers  (2025-04-13T15:53:30Z)
• Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks [18.95453617434051]
  Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. New music pieces or artists often face the cold-start problem due to insufficient initial information. To address this, one can extract content-based information directly from the music to enhance collaborative-filtering-based methods.
  arXiv  Detail & Related papers  (2024-09-13T17:53:06Z)
• Comparative Analysis of Pretrained Audio Representations in Music Recommender Systems [0.0]
  Music Information Retrieval (MIR) has proposed various models pretrained on large amounts of music data. transfer learning showcases the proven effectiveness of pretrained backend models with a broad spectrum of downstream tasks. Music Recommender Systems tend to favour traditional end-to-end neural network learning over pretrained models.
  arXiv  Detail & Related papers  (2024-09-13T17:03:56Z)
• Enhancing Sequential Music Recommendation with Personalized Popularity Awareness [56.972624411205224]
  This paper introduces a novel approach that incorporates personalized popularity information into sequential recommendation. Experimental results demonstrate that a Personalized Most Popular recommender outperforms existing state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-06T15:05:12Z)
• Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models [49.458914600467324]
  We introduce the Schr"odinger Bridge into diffusion-based sequential recommendation models, creating the SdifRec model. We also propose an extended version of SdifRec called con-SdifRec, which utilizes user clustering information as a guiding condition.
  arXiv  Detail & Related papers  (2024-08-30T09:10:38Z)
• Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation [12.295794664393368]
  We introduce PISA, a session-level sequential recommender system for music streaming services. PISA employs a Transformer architecture learning embedding representations of listening sessions and users. We demonstrate the empirical relevance of PISA using both publicly available listening data from Last.fm and proprietary data from Deezer.
  arXiv  Detail & Related papers  (2024-08-29T14:44:12Z)
• Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study [33.10311742703679]
  We make the first attempt to investigate the benefits of pre-training on sound generation with AudioLDM. Our study demonstrates the advantages of the pre-trained AudioLDM, especially in data-scarcity scenarios. We benchmark the sound generation task on various frequently-used datasets.
  arXiv  Detail & Related papers  (2023-03-07T12:49:45Z)
• Effective and Efficient Training for Sequential Recommendation using Recency Sampling [91.02268704681124]
  We propose a novel Recency-based Sampling of Sequences training objective. We show that the models enhanced with our method can achieve performances exceeding or very close to stateof-the-art BERT4Rec.
  arXiv  Detail & Related papers  (2022-07-06T13:06:31Z)
• ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation [28.22402119581332]
  Session-based recommendation has received growing attention recently due to the increasing privacy concern. We propose a method called Adaptively Distilled Exemplar Replay (ADER) by periodically replaying previous training samples. ADER consistently outperforms other baselines, and it even outperforms the method using all historical data at every update cycle.
  arXiv  Detail & Related papers  (2020-07-23T13:19:53Z)

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.