@inproceedings{epure-etal-2024-harnessing,
title = "Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation",
author = "Epure, Elena V. and
Brocal, Gabriel Meseguer and
Afchar, Darius and
Hennequin, Romain",
editor = "Kruspe, Anna and
Oramas, Sergio and
Epure, Elena V. and
Sordo, Mohamed and
Weck, Benno and
Doh, SeungHeon and
Won, Minz and
Manco, Ilaria and
Meseguer-Brocal, Gabriel",
booktitle = "Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)",
month = nov,
year = "2024",
address = "Oakland, USA",
publisher = "Association for Computational Lingustics",
url = "https://aclanthology.org/2024.nlp4musa-1.4/",
pages = "17--24",
abstract = "Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categories or consumption contexts, from training data or user preferences. This has been proven effective in domains like movies or products. In music though, understanding how effectively LMs utilize song descriptors for natural language-based music recommendation is relatively limited. In this paper, we assess LMs effectiveness in recommending songs based on user natural language requests and items with descriptors like genres, moods, and listening contexts. We formulate the recommendation as a dense retrieval problem and assess LMs as they become increasingly familiar with data pertinent to the task and domain. Our findings reveal improved performance as LMs are fine-tuned for general language similarity, information retrieval, and mapping longer descriptions to shorter, high-level descriptors in music."
}
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<abstract>Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categories or consumption contexts, from training data or user preferences. This has been proven effective in domains like movies or products. In music though, understanding how effectively LMs utilize song descriptors for natural language-based music recommendation is relatively limited. In this paper, we assess LMs effectiveness in recommending songs based on user natural language requests and items with descriptors like genres, moods, and listening contexts. We formulate the recommendation as a dense retrieval problem and assess LMs as they become increasingly familiar with data pertinent to the task and domain. Our findings reveal improved performance as LMs are fine-tuned for general language similarity, information retrieval, and mapping longer descriptions to shorter, high-level descriptors in music.</abstract>
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%0 Conference Proceedings
%T Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation
%A Epure, Elena V.
%A Brocal, Gabriel Meseguer
%A Afchar, Darius
%A Hennequin, Romain
%Y Kruspe, Anna
%Y Oramas, Sergio
%Y Epure, Elena V.
%Y Sordo, Mohamed
%Y Weck, Benno
%Y Doh, SeungHeon
%Y Won, Minz
%Y Manco, Ilaria
%Y Meseguer-Brocal, Gabriel
%S Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)
%D 2024
%8 November
%I Association for Computational Lingustics
%C Oakland, USA
%F epure-etal-2024-harnessing
%X Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categories or consumption contexts, from training data or user preferences. This has been proven effective in domains like movies or products. In music though, understanding how effectively LMs utilize song descriptors for natural language-based music recommendation is relatively limited. In this paper, we assess LMs effectiveness in recommending songs based on user natural language requests and items with descriptors like genres, moods, and listening contexts. We formulate the recommendation as a dense retrieval problem and assess LMs as they become increasingly familiar with data pertinent to the task and domain. Our findings reveal improved performance as LMs are fine-tuned for general language similarity, information retrieval, and mapping longer descriptions to shorter, high-level descriptors in music.
%U https://aclanthology.org/2024.nlp4musa-1.4/
%P 17-24
Markdown (Informal)
[Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation](https://aclanthology.org/2024.nlp4musa-1.4/) (Epure et al., NLP4MusA 2024)
ACL