# Predicting User Intents and Musical Attributes from Music Discovery Conversations

arXiv: https://arxiv.org/abs/2411.12254 · HTML: https://mlsanity.com/paper/2411.12254/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking](https://mlsanity.com/paper/2311.06102/) (2023)
- [Bi-directional Joint Neural Networks for Intent Classification and Slot Filling](https://mlsanity.com/paper/2202.13079/) (2022)
- [Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset](https://mlsanity.com/paper/2303.06791/) (2023)
- [Parallel Intent and Slot Prediction using MLB Fusion](https://mlsanity.com/paper/2003.09211/) (2020)
- [Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models](https://mlsanity.com/paper/2411.07439/) (2024)

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

- [TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation](https://mlsanity.com/paper/2509.09685/) (2025)

## Abstract (arXiv)

Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains, it has not received much attention in the music domain. In this paper, we investigate intent classification models for music discovery conversation, focusing on pre-trained language models. Rather than only predicting functional needs: intent classification, we also include a task for classifying musical needs: musical attribute classification. Additionally, we propose a method of concatenating previous chat history with just single-turn user queries in the input text, allowing the model to understand the overall conversation context better. Our proposed model significantly improves the F1 score for both user intent and musical attribute classification, and surpasses the zero-shot and few-shot performance of the pretrained Llama 3 model.