# Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation

arXiv: https://arxiv.org/abs/2411.05649 · HTML: https://mlsanity.com/paper/2411.05649/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation](https://mlsanity.com/paper/2507.15826/) (2025)

## Abstract (arXiv)

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. However, in the music domain, 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 descriptions and items with descriptors like genres, moods, and listening contexts. We formulate the recommendation task 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.