LLM-Based Music Recommendation System

• LLM-based music recommendation systems leverage large language models to interpret user queries and generate personalized music suggestions.
• They integrate multimodal features from audio, lyrics, metadata, and semantic tags to enable context-rich and dynamic retrieval.
• These systems employ advanced dialogue and tool-calling architectures to enhance usability, evaluation, and transparency in music discovery.

LLM-based music recommendation systems utilize LLMs to interpret complex user intent, generate music queries, and orchestrate advanced retrieval techniques for personalized recommendations. These systems represent a major shift from traditional recommendation approaches, leveraging natural language understanding, multimodal embeddings, explicit tool integration, and conversational frameworks to deliver context-aware, user-driven music discovery.

1. System Architectures and Paradigms

LLM-based music recommender architectures are characterized by the central role of the LLM, which acts as either a query interpreter, an end-to-end generator, or a tool planner orchestrating modular retrieval components. Prominent system blueprints include:

• End-to-End Dialogue Models: In systems such as TALKPLAY (Doh et al., 19 Feb 2025), the LLM ingests multimodal music tokens and user queries, unifying playlist generation and dialogue modeling into a single next-token prediction problem. Vocabulary expansion enables the model to handle both linguistic and music-relevant tokens.
• Unified Tool-calling Pipelines: TalkPlay-Tools (Doh et al., 2 Oct 2025) positions the LLM as a planner, orchestrating sequential calls to external retrieval modules (SQL, BM25, dense embedding, semantic ID retrieval), supporting structured queries, semantic search, and generative retrieval within multi-turn recommendation dialogues.
• Agentic Multi-Agent Systems: LLM-powered frameworks may leverage multiple specialized agents (e.g., Reading Agent, Analysis Agent, Recommendation Agent) as in (Boadana et al., 7 Aug 2025), each handling a distinct subtask, with the LLM coordinating collaborative decision workflows and API communications.
• Translation-based Multimodal Models: JAM (Melchiorre et al., 21 Jul 2025) models user-query-item interactions as vector translations in a shared latent space, inspired by knowledge graph embedding techniques like TransE, providing lightweight, scalable integration atop existing collaborative and content-based filtering infrastructure.

These architectures are supported by synthetic data pipelines (TalkPlayData 2 (Choi et al., 18 Aug 2025)), multimodal tokenizers, and open-form agentic simulation frameworks, enabling robust training and evaluation of generative recommendation models.

2. Multimodal Feature Integration

Modern LLM-based recommenders leverage multimodal representations, enabling fine-grained personalization and context-aware recommendation:

Modality	Example Feature Extraction	Aggregation Strategy
Audio	MusicFM, Whisper (embedding/vq)	K-means cluster tokens, embeddings
Lyrics	NV-Embed-v2, LLM summaries	Text expansion, dense similarity
Metadata	Tag, artist, album, year	SQL filtering, sparse BM25
Semantic Tags	Genre, mood, instrument	Cross-attention, expert gating
Playlist Co-occurrence	Word2vec-style embeddings	Aggregated vectors, collaborative filtering

Both token-based (TALKPLAY) and embedding-based (JAM, CrossMuSim (Tsoi et al., 29 Mar 2025)) pipelines dynamically weight and combine features using cross-attention, mixture-of-experts, or late fusion (e.g., $s = \alpha s_{audio} + (1-\alpha) s_{lyrics}$ (Zeng et al., 3 Jul 2025)) to accommodate user queries referencing diverse musical facets.

3. Retrieval, Filtering, and Generation Techniques

LLM-powered recommenders interleave multiple retrieval paradigms:

• Boolean Filtering (SQL): Structured queries for attribute selection (e.g., filtering by genre, release year) (Doh et al., 2 Oct 2025).
• Sparse Retrieval (BM25): Lexical matching for precise keywords, suitable for artist- and title-specific queries.
• Dense Retrieval: Semantic similarity via text or multimodal embeddings; bi-encoders and contrastive learning for robust query-song alignment (Epure et al., 8 Nov 2024, Tsoi et al., 29 Mar 2025).
• Generative Retrieval: LLMs directly decode track identifiers in response to natural language prompts, employing CF-based semantic IDs for compact, efficient mapping (Palumbo et al., 31 Mar 2025).

This unified retrieval-reranking approach allows selective application of retrieval modules, optimizing recommendation relevance while supporting cold-start and personalization scenarios.

4. Conversational Personalization and User Experience

LLM-based systems have extended recommendation into actively user-driven, multi-turn conversational formats (Yun et al., 21 Feb 2025, Choi et al., 18 Aug 2025). Notable features include:

• Natural Language Interaction: Users articulate needs through open-ended, context-rich dialogues; LLM provides recommendations and explanations.
• Clarification of Implicit Needs: Systems can interpret indirect cues—emotional descriptions, images, situational statements—to crystallize and address evolving musical preferences.
• Customizable Recommendation Logic: Users may specify feedback mechanisms, scenario criteria, or personalized data inputs, with LLMs supporting unique exploration and introspective preference discovery.

Multi-agent and pipeline architectures achieve deeper reflection of user taste and iterative improvement of recommendation logic across sessions.

5. Training Data, Evaluation, and Performance Metrics

Benchmarks and datasets underpinning LLM-based systems include:

• Synthetic Conversational Data: Agentic pipelines (TalkPlayData 2) generate multimodal, goal-conditioned dialogues for generative model training and evaluation.
• Real-world User Studies: Controlled experiments compare LLM-based profiles and recommendations against collaborative filtering, TF-IDF models, and BM25, employing quantitative metrics such as Hit@K, Recall, NDCG, playlist ratings, and likability.
• Subjective and Automated Rating: Both human evaluation (Likert scales, identification ratings) and LLM-as-a-Judge scoring (e.g., Gemini 2.5 Pro) assess conversational realism, recommendation quality, and profile fidelity.
• Statistical Findings: Empirical data reveal LLM-driven agents (e.g., LLaMA) achieve up to 89.32% like rates and high playlist ratings, with nuanced trade-offs between satisfaction, novelty, and computational efficiency (Boadana et al., 7 Aug 2025).

Latencies, computational loads, cold-start robustness, and cross-cultural generalization are scrutinized alongside core recommendation effectiveness.

6. Biases, Interpretability, and Profile Generation

LLM-generated natural language taste profiles provide interpretable, editable representations (Sguerra et al., 22 Jul 2025):

• Bias Analysis: Model- and data-driven biases influence profile depth, stylistic tone, and genre representation, with some genres (e.g., rap) systematically rated lower and others (metal) higher, regardless of true user taste.
• Transparency and Control: Profiles offer scrutable alternatives to collaborative filtering’s opaque embeddings, granting users direct control over how their preferences are modeled.
• Cold-Start Handling: NL profiles can boost system robustness with limited consumption data, but attention to hallucinations, overfitting, and alignment between subjective self-identification and downstream recommendation effectiveness is critical.
• Decoupled Optimization: A plausible implication is that future systems may separate user-facing profile summaries from optimization objectives for ranking and personalization, to balance interpretability, trust, and algorithmic performance.

7. Future Directions and Research Opportunities

LLM-based recommenders continue to evolve along several axes:

• Enhanced Tool Calling and Dynamic Orchestration: Increasing the repertoire and adaptivity of LLM-planned retrieval modules may improve context-awareness and precision in multi-turn dialogues.
• Advances in Multimodal Fusion: Richer cross-modal embeddings and advanced attention/gating mechanisms will further personalize recommendations, as the number and diversity of modalities increase.
• Bias Mitigation and Debiasing: Targeted fine-tuning, explicit debiasing, and broader user studies can improve fairness and generalizability, especially across culturally diverse music catalogs.
• Scalability and Efficiency: Improving computational efficiency—through compact semantic IDs, parameter sharing, and hybrid retrieval—remains pivotal for large-scale deployment.
• Agentic Synthetic Data Generation: Synthetic data pipelines supporting multi-agent, multimodal conversation logging (TalkPlayData 2) provide the foundation for scalable training of generative conversational recommenders, enabling more realistic, contextually rich system evaluations.

Emerging research demonstrates that LLM-based music recommendation integrates sophisticated language understanding, multimodal representations, modular retrieval orchestration, robust evaluation, and user-driven interaction design, collectively advancing the field toward highly context-sensitive and transparent recommendation systems.