TalkPlay-Tools: LLM-Driven Music Recommendation

• TalkPlay-Tools is a unified LLM-driven framework for conversational music recommendation, integrating multiple specialized retrieval and filtering components.
• It employs a multi-stage, chain-of-thought planning process to orchestrate Boolean, sparse, dense, and generative retrieval tools for precise recommendation execution.
• Empirical analysis shows enhanced Hit@K metrics compared to baseline methods, demonstrating its effectiveness in handling multimodal, complex queries.

TalkPlay-Tools refers to a unified, LLM-driven framework for conversational music recommendation in which the LLM orchestrates a pipeline of specialized retrieval and filtering tools to interpret user intent, plan tool invocations, and deliver ranked music suggestions (Doh et al., 2 Oct 2025). Developed to overcome limitations in generative recommenders that fail to utilize crucial database filtering and multimodal components, TalkPlay-Tools implements an explicit multi-stage retrieval-reranking architecture: the LLM interprets the user’s query and context, generates a structured plan for tool use, coordinates execution of Boolean, sparse, dense, and generative retrieval modules, and synthesizes a natural language response. This integration supports diverse modalities and complex conversational recommendation scenarios, combining the reasoning flexibility of generative models with the precision of conventional retrieval systems.

1. System Architecture and Workflow

TalkPlay-Tools comprises two main subsystems: a Music Recommendation Agent powered by an LLM, and an External Environment that executes tool calls. The LLM receives the user query $q_t$, conversation state $s_{t-1}$, and user profile $p_u$, then constructs a sequence of tool calls $C_t$ using a specialized prompt ($P_{tool}$). These calls—each specifying a tool and its arguments—mirror a staged retrieval-reranking pipeline. The External Environment then executes $C_t$ over the item database $\mathcal{D}$, and the LLM generates a conversational response $r_t$ using the outputs $m_t$.

Mathematically, the process is formalized by:

• $C_t = \text{LLM}(q_t, s_{t-1}, p_u; P_{tool}, T)$
• $m_t = \text{ToolEnv}(C_t; D)$
• $$r_t = \text{LLM}(m_t, q_t, s_{t-1}, p_u; P_{response})$$

Planning, retrieval, and reranking are decoupled and explicitly represented in the prompt structure. Robust retry strategies ensure pipeline completion even when tool failures (e.g., incorrect SQL syntax) occur.

2. Tool Calling Mechanism and Orchestration

The core innovation is the LLM-driven, chain-of-thought-based planning and orchestration of multiple retrieval tools. For each query, the LLM determines:

• Which tools are required (e.g., SQL for attribute filtering, BM25 for sparse lexical retrieval, embedding similarity for dense retrieval, semantic ID matching for generative retrieval)
• The execution order (e.g., filtering before reranking)
• Precise arguments for each tool

Example tool call planning for a query “minimal ambient music by artist X”:

1. BM25: Lexical retrieval on artist constraint
2. Dense retrieval: Embedding-based semantic match on “minimal ambient”
3. Reranking: Synthesize top candidates for final recommendation

Each stage’s output informs subsequent stages. The orchestration adapts based on interpretation of user intent and conversation context, including feedback from previous turns and explicit user profile data.

3. Specialized Retrieval and Filtering Components

TalkPlay-Tools integrates four distinct tool types:

• Boolean Filtering (SQL): Executes structured queries over metadata fields (title, artist, album, release year, BPM, key) for precision. Success rate is observed at 27.4%, indicating notable syntactic challenges.
• Sparse Retrieval (BM25): Employs keyword matching over lowercase-converted fields (e.g., artist, title) for explicit lexical queries.
• Dense Retrieval (Embedding Similarity): Utilizes models (Qwen3, CLAP, SigLIP2) for:
  • Text-to-item similarity (semantic queries)
  • Item-to-item similarity (reference track clustering)
  • User-to-item personalization (embeddings from Bayesian Personalized Ranking)
• Generative Retrieval (Semantic IDs): Represents music items with discrete “semantic IDs” via residual vector quantization, enabling in-context generative matching across modalities (audio, lyrics, artwork).

Table: Summary of Retrieval Components

Tool Type	Modality/Constraint	Implementation
Boolean (SQL)	Metadata, numeric/categorical	SQL query engine
Sparse (BM25)	Keyword constraint	BM25 lexical indexing
Dense (Embeddings)	Semantic, similarity	Pretrained deep embedding models
Generative (IDs)	Cross-modal, context	Residual vector quantization

Each component supports a distinct query modality, enabling granular filtering, broad semantic matching, or multimodal synthesis as required.

4. User Intent Interpretation and Contextualization

User intent is inferred by the LLM from multi-turn dialogue, explicit query signals, user profile ($p_u$ includes age, listening history), and prior conversation state ($s_{t-1}$). The LLM’s chain-of-thought reasoning determines whether the preference is best served by exact filtering, keyword search, semantic similarity, or multimodal associations. For ambiguous or layered queries, the system plans multi-step tool invocation (e.g., filter–retrieve–rerank). This deep contextualization is critical for dynamic, personalized recommendations.

5. Performance and Empirical Analysis

TalkPlay-Tools is evaluated via standard recommendation metrics, particularly Hit@K scores for $K=1,10,20$. Notably, Hit@1 improves from 0.018 (BM25 baseline) to 0.022 using the system’s unified tool-calling framework. Analysis of tool call frequencies reveals SQL and BM25 as dominant, but SQL’s lower success rate (27.4%) highlights robustness challenges and motivates integrated retry mechanisms. Overall, the system demonstrates performance gains over single-method and naïve LLM+BM25 baselines, especially in zero-shot scenarios (Doh et al., 2 Oct 2025).

6. Innovations and Contributions

TalkPlay-Tools introduces substantial advancements for conversational recommendation systems:

• Unified sequential integration of heterogeneous retrieval methods coordinated via LLM planning
• Multi-stage tool orchestration based on prompt-driven chain-of-thought, with execution order dynamically determined per query
• Multimodal synthesis combining structured metadata, text, audio, and image features
• Empirical analysis of tool efficacy, call frequency, and success/failure rates, informing future reinforcement learning or prompt refinement approaches

These innovations enable the system to handle complex conversational requirements that span the spectrum from precise attribute search to nuanced semantic retrieval, positioning TalkPlay-Tools as an adaptable backbone for next-generation recommender systems.

7. Connections to Related Agentic and Data Pipeline Frameworks

Beyond the main framework (Doh et al., 2 Oct 2025), related lines of research provide contextual relevance:

• Agentic synthetic data pipelines for multimodal conversational recommendation (TalkPlayData 2) (Choi et al., 18 Aug 2025) leverage role-specialized LLMs for generating diverse recommendation dialogues, which serve as training data for systems like TalkPlay-Tools.
• Zero-shot tool instruction optimization via PLAY2PROMPT (Fang et al., 18 Mar 2025) allows systems to learn tool usage and documentation through trial and error, enhancing generalizability.
• Multi-agent architectures and memory systems (OPEN-THEATRE) (Xu et al., 20 Sep 2025) provide methods for narrative and long-term dialogue coherence, indirectly informing design principles for conversational recommenders.

A plausible implication is that future work may further tighten the integration between agentic data generation, zero-shot tool learning, and multimodal orchestration, extending the capabilities and robustness of frameworks in the TalkPlay-Tools paradigm.

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TalkPlay-Tools thus embodies a rigorous, LLM-driven integration of specialized retrieval and filtering components for context-aware, multimodal conversational music recommendation, with demonstrated empirical advantages and significant architectural innovations appropriate for advanced recommender system research.