@inproceedings{buzaev-etal-2026-learning,
title = "Learning When to Personalize: {LLM} Based Playlist Generation via Query Taxonomy and Classification",
author = "Buzaev, Fedor and
Sukharev, Ivan and
Mullahmetov, Rinat and
Bogachev, Roman and
Sedunov, Ilya and
Pavlovich, Oleg and
Pugacheva, Daria",
editor = "Epure, Elena V. and
Oramas, Sergio and
Doh, SeungHeon and
Ramoneda, Pedro and
Kruspe, Anna and
Sordo, Mohamed",
booktitle = "Proceedings of the 4th Workshop on {NLP} for Music and Audio ({NLP}4{M}us{A} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4musa-1.8/",
pages = "51--57",
ISBN = "979-8-89176-369-2",
abstract = "Playlist generation based on textual queries using large language models (LLMs) is becoming an important interaction paradigm for music streaming platforms. User queries span a wide spectrum from highly personalized intent to essentially catalog-style requests. Existing systems typically rely on non-personalized retrieval/ranking or apply a fixed level of preference conditioning to every query, which can overfit catalog queries to a single user or under-personalize explicitly listener-dependent requests. We present an industrial-scale LLM-based playlist generation system with dynamic personalization that adapts the personalization strength to the query type. We define a query taxonomy, train a query-type classifier on 5,000 manually labeled queries, and use its predicted probability to modulate the mixture of LLM-based semantic scoring and personalized evaluation. In a blind user study with pairwise comparisons and ELO aggregation, this approach consistently outperforms both non-personalized and fixed-personalization baselines."
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<abstract>Playlist generation based on textual queries using large language models (LLMs) is becoming an important interaction paradigm for music streaming platforms. User queries span a wide spectrum from highly personalized intent to essentially catalog-style requests. Existing systems typically rely on non-personalized retrieval/ranking or apply a fixed level of preference conditioning to every query, which can overfit catalog queries to a single user or under-personalize explicitly listener-dependent requests. We present an industrial-scale LLM-based playlist generation system with dynamic personalization that adapts the personalization strength to the query type. We define a query taxonomy, train a query-type classifier on 5,000 manually labeled queries, and use its predicted probability to modulate the mixture of LLM-based semantic scoring and personalized evaluation. In a blind user study with pairwise comparisons and ELO aggregation, this approach consistently outperforms both non-personalized and fixed-personalization baselines.</abstract>
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%0 Conference Proceedings
%T Learning When to Personalize: LLM Based Playlist Generation via Query Taxonomy and Classification
%A Buzaev, Fedor
%A Sukharev, Ivan
%A Mullahmetov, Rinat
%A Bogachev, Roman
%A Sedunov, Ilya
%A Pavlovich, Oleg
%A Pugacheva, Daria
%Y Epure, Elena V.
%Y Oramas, Sergio
%Y Doh, SeungHeon
%Y Ramoneda, Pedro
%Y Kruspe, Anna
%Y Sordo, Mohamed
%S Proceedings of the 4th Workshop on NLP for Music and Audio (NLP4MusA 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-369-2
%F buzaev-etal-2026-learning
%X Playlist generation based on textual queries using large language models (LLMs) is becoming an important interaction paradigm for music streaming platforms. User queries span a wide spectrum from highly personalized intent to essentially catalog-style requests. Existing systems typically rely on non-personalized retrieval/ranking or apply a fixed level of preference conditioning to every query, which can overfit catalog queries to a single user or under-personalize explicitly listener-dependent requests. We present an industrial-scale LLM-based playlist generation system with dynamic personalization that adapts the personalization strength to the query type. We define a query taxonomy, train a query-type classifier on 5,000 manually labeled queries, and use its predicted probability to modulate the mixture of LLM-based semantic scoring and personalized evaluation. In a blind user study with pairwise comparisons and ELO aggregation, this approach consistently outperforms both non-personalized and fixed-personalization baselines.
%U https://aclanthology.org/2026.nlp4musa-1.8/
%P 51-57
Markdown (Informal)
[Learning When to Personalize: LLM Based Playlist Generation via Query Taxonomy and Classification](https://aclanthology.org/2026.nlp4musa-1.8/) (Buzaev et al., NLP4MusA 2026)
ACL