Exploiting Device and Audio Data to Tag Music with User-Aware Listening Contexts

• URL: http://arxiv.org/abs/2211.07250v1
• Date: Mon, 14 Nov 2022 10:08:12 GMT
• Title: Exploiting Device and Audio Data to Tag Music with User-Aware Listening Contexts
• Authors: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, Ga\"el Richard
• Abstract summary: We propose a system which can generate a situational playlist for a user at a certain time by leveraging user-aware music autotaggers. Experiments show that such a context-aware personalized music retrieval system is feasible, but the performance decreases in the case of new users.
• Score: 8.224040855079176
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As music has become more available especially on music streaming platforms, people have started to have distinct preferences to fit to their varying listening situations, also known as context. Hence, there has been a growing interest in considering the user's situation when recommending music to users. Previous works have proposed user-aware autotaggers to infer situation-related tags from music content and user's global listening preferences. However, in a practical music retrieval system, the autotagger could be only used by assuming that the context class is explicitly provided by the user. In this work, for designing a fully automatised music retrieval system, we propose to disambiguate the user's listening information from their stream data. Namely, we propose a system which can generate a situational playlist for a user at a certain time 1) by leveraging user-aware music autotaggers, and 2) by automatically inferring the user's situation from stream data (e.g. device, network) and user's general profile information (e.g. age). Experiments show that such a context-aware personalized music retrieval system is feasible, but the performance decreases in the case of new users, new tracks or when the number of context classes increases.

Related papers

• SoundSignature: What Type of Music Do You Like? [0.0]
  SoundSignature is a music application that integrates a custom OpenAI Assistant to analyze users' favorite songs. The system incorporates state-of-the-art Music Information Retrieval (MIR) Python packages to combine extracted acoustic/musical features with the assistant's extensive knowledge of the artists and bands.
  arXiv  Detail & Related papers  (2024-10-04T12:40:45Z)
• System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes [80.97898201876592]
  We propose a generative model in which past content interactions impact the arrival rates of users based on a self-exciting Hawkes process. We show analytically that given samples it is possible to disentangle System-1 and System-2 and allow content optimization based on user utility.
  arXiv  Detail & Related papers  (2024-05-29T18:19:37Z)
• MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models [54.55063772090821]
  MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements. The primary goal of this system is to free users from the intricacies of AI-music tools, enabling them to concentrate on the creative aspect.
  arXiv  Detail & Related papers  (2023-10-18T13:31:10Z)
• 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)
• IteraTTA: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models [40.798454815430034]
  IteraTTA is designed to aid users in refining text prompts and selecting favorable audio priors from the generated audios. Our implementation and discussions highlight design considerations that are specifically required for text-to-audio models.
  arXiv  Detail & Related papers  (2023-07-24T11:00:01Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Psychologically-Inspired Music Recommendation System [3.032299122358857]
  We seek to relate the personality and the current emotional state of the listener to the audio features in order to build an emotion-aware MRS. We compare the results both quantitatively and qualitatively to the output of the traditional MRS based on the Spotify API data to understand if our advancements make a significant impact on the quality of music recommendations.
  arXiv  Detail & Related papers  (2022-05-06T19:38:26Z)
• Explainability in Music Recommender Systems [69.0506502017444]
  We discuss how explainability can be addressed in the context of Music Recommender Systems (MRSs) MRSs are often quite complex and optimized for recommendation accuracy. We show how explainability components can be integrated within a MRS and in what form explanations can be provided.
  arXiv  Detail & Related papers  (2022-01-25T18:32:11Z)
• A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps [1.6050172226234583]
  We present the system recently deployed on the music streaming service Deezer to address this problem. The solution leverages a semi-personalized recommendation strategy, based on a deep neural network architecture. We extensively show the practical impact of this system and its effectiveness at predicting the future musical preferences of cold start users on Deezer.
  arXiv  Detail & Related papers  (2021-06-07T17:35:44Z)
• A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles [55.951126447217526]
  We study personalized Task-oriented Dialogue Systems without assuming that user profiles are complete. We propose a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually enrich user profiles. CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3.06% in terms of response selection accuracy.
  arXiv  Detail & Related papers  (2021-02-16T18:05:54Z)
• 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)

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.