LLM-Powered User Interest Exploration

• LLM-powered user interest exploration systems are intelligent frameworks that automatically detect, cluster, and interpret user behavior using advanced language models.
• They employ multi-stage architectures integrating LLM cores, domain-specific engines, and feedback loops to adapt recommendations and data analytics across various domains.
• Key techniques like few-shot prompting, fine-tuning, and retrieval-augmented generation enable dynamic, interpretable interest modeling and scalable real-time performance.

A LLM-powered user interest exploration system is a class of intelligent system that leverages the reasoning, abstract understanding, and generative capacities of LLMs to automatically discover, organize, and interpret user interests from behavioral data. These systems are designed to improve user modeling, recommendation, exploratory analysis, and assistive user interfaces across a wide variety of domains—from recommender systems to data analytics—by directly incorporating LLM-driven intent extraction, structured reasoning, and dynamic adaptability. The following sections provide an in-depth, technically rigorous overview of architectures, methodologies, mathematical models, challenges, applications, and performance characteristics found in the most recent literature in this area.

1. System Architectures and Modular Components

LLM-powered interest exploration systems typically follow a multi-stage pipeline architecture, integrating LLM modules with domain-specific engines, feedback mechanisms, and user interfaces. Representative systems include InsightPilot (Ma et al., 2023), the journey extraction and naming framework (Christakopoulou et al., 2023), and hybrid hierarchical recommenders (Wang et al., 25 May 2024). A generalized architecture decomposes as follows:

• User Interface: Accepts high-level, natural language or multimodal queries, serving both novice and expert users. Some systems extend this with schema-like or GUI elements for decomposed sub-tasks (e.g., ExploreLLM (Ma et al., 2023), SLEGO (Ng et al., 17 Jun 2024)).
• LLM Core: At the core, LLMs power the interpretation of user intent, reasoning over context and history to sequence the next exploratory or recommendation actions, generate “interest cluster” candidates, or synthesize natural language interest summaries.
• Domain Engines: An insight engine (e.g., QuickInsight, MetaInsight, XInsight in (Ma et al., 2023)) or a recommendation engine (e.g., transformer-based sequential recommenders in (Wang et al., 25 May 2024)) provides lower-level, scalable computations or retrieval, grounded in the domain’s data graph.
• Bridging Entities: Interest Clusters, Intentional Queries (IQueries), or Journey Clusters act as abstractions for managing granularity, controlling LLM outputs, and mapping between language-level decisions and item-level actions.
• Feedback and Personalization Layers: Feedback-driven reranking, user experience interviews (CLUE (Liu et al., 21 Feb 2025)), and iterative query refinement. Multi-agent frameworks (CARE (Peng et al., 31 Oct 2024)) or two-stage hybrid update strategies (fine-tuning + RAG (Meng et al., 23 Oct 2025)) further decouple reasoning and data freshness.

This modularity allows systems to scale, integrate new models, and adapt to a variety of application domains and changing user populations.

2. Interest Representation, Clustering, and Reasoned Sequencing

LLM-based systems depart from static embeddings by reasoning about user interests as dynamic, interpretable structures:

• Interest Clusters: Items are grouped into “interest clusters” via language-driven clustering (e.g., ICPC in (Christakopoulou et al., 2023), capsule-based collab clusters in (Wang et al., 15 Jul 2025), or traffic-weighted clustering in (Wang et al., 25 May 2024)). These clusters can be at several semantic levels (from broad categories to fine subtopics), controlled via hierarchical clustering and attention-based granularity adjustment.

$$\mathcal{C}^l = \{ c_1^l, c_2^l, ..., c_{M_l}^l \}, \quad l = 1,...,L$$

• Intentional Queries and Action Sequencing: InsightPilot (Ma et al., 2023) formalizes “analysis action” chains, where each action transforms existing insights into new insights, constructing a data exploration sequence:

$$S = \langle AA_{\text{init}}, \text{Insight}_1, AA_1, ..., AA_n, \perp \rangle$$

• Journey Extraction and Naming: Sequential user activity is clustered into persistent “interest journeys” (ICPC algorithm), which are then summarized into interpretable journey names via LLM few-shot prompting, soft prompt tuning, or full model fine-tuning (Christakopoulou et al., 2023).

This flexible, layered abstraction makes it feasible to model user interests at different resolutions, and critically, to explain or control the exploration process.

3. Core LLM Techniques and Update Strategies

LLM modules in interest exploration systems are adapted through a variety of approaches:

• Few-Shot Prompting & Prompt Tuning: In naming, labeling, and structured reasoning tasks, prompt engineering with a limited set of domain-specific examples achieves robust, context-driven outputs. Learned soft prompts improve generalization—especially on “in-the-wild” data (Christakopoulou et al., 2023).
• Supervised Fine-tuning: When sufficient data exists or domain alignment is required, LLMs are fine-tuned on transition tuples, journey–label pairs, or pipeline configurations (Wang et al., 25 May 2024, Meng et al., 23 Oct 2025).
• Retrieval-Augmented Generation (RAG): RAG supports rapid incorporation of dynamic user interests and changing content, allowing the system to “inject” fresh clusters, histories, or trends into the LLM prompt on a daily or even per-session basis (Meng et al., 23 Oct 2025). This hybrid update strategy balances the deep adaptation of slow-cadence retraining with the agility of prompt-based data augmentation, and is empirically verified to improve user satisfaction and hit rates.
• Dual-Model Decoupling: Decoupling novelty from user-alignment through training separate LLMs (a novelty model for cluster exploration, an alignment model for preference fit) with inference-time scaling and best-of-n selection avoids reward overfitting and preserves both diversity and relevance of recommendations (Wang et al., 7 Apr 2025).

4. Evaluation Metrics, Performance, and Practical Scalability

Evaluation in LLM-powered interest exploration systems includes both traditional and novel user-centric metrics:

• Exploration and Diversity Metrics: Ratio of novel interest cluster impressions, unique engaged user-cluster (UEUC) count, user cluster coverage ($\text{UCI}@N$), and lifted diversity in recommendations (Wang et al., 25 May 2024, Wang et al., 7 Apr 2025).
• Recommendation Quality and Satisfaction: Positive playback rate, completion rate, hit rate, and standard ranking metrics (Recall@K, NDCG@K) (Meng et al., 23 Oct 2025, Qiao et al., 14 Nov 2024, Wang et al., 15 Jul 2025).
• User Study Metrics: Quantitative scales for relevance, completeness, understandability (e.g., in comparison with OpenAI Code Interpreter and Langchain Pandas Agent (Ma et al., 2023)); cognitive load, transparency, user preference in structured interfaces (CARE (Peng et al., 31 Oct 2024), ExploreLLM (Ma et al., 2023)).
• Feedback Loops and User Simulation: LLM-based simulators and explicit alignment with collective user feedback (clicks, dwell time, likes) directly inform the model’s adaptation (Wang et al., 7 Apr 2025, Liu et al., 21 Feb 2025).

Empirically, hybrid architectures have demonstrated increased exploring diversity, improved user metrics, and scalability to billion-user platforms while maintaining practical latency and update costs.

5. Notable Applications and Domain Extensions

LLM-powered user interest exploration principles have been adapted to a diverse range of applications:

Domain	Representative Paper(s)	Characteristic Approach
Automated Data Analysis	(Ma et al., 2023)	LLM-directed multi-step exploration, insight sequencing
Journey-aware Recommendation	(Christakopoulou et al., 2023)	Personalized cluster extraction & descriptive labeling with LLM
Conversational Music Recommendation	(Yun et al., 21 Feb 2025)	Multi-turn CRS for unique exploration and user reflection
Personalized Analytics Pipeline	(Ng et al., 17 Jun 2024)	Modular microservices, knowledge base, LLM for adaptation
Geo-spatial Exploration	(Deng et al., 9 Jul 2025)	LLM agents with map analysis, RAG for itinerary adaptation
Adaptive Update Strategies	(Meng et al., 23 Oct 2025)	Hybrid fine-tuning + RAG; daily vs. monthly update cycles

These systems have shown efficacy not just in recommendation and analytics, but also in collaborative, human-in-the-loop interfaces and new forms of exploration (e.g., user-driven feedback interviews (Liu et al., 21 Feb 2025), collaborative trip planning (Ma et al., 2023, Deng et al., 9 Jul 2025)).

6. Technical Challenges and Prospects

While LLM-driven systems address many limitations of traditional user modeling, several key challenges and solutions have emerged:

• Data Sparsity and Modal Alignment: Multi-level clustering (user-individual and user-crowd), modality alignment losses, and large-scale synthesized user cliques help overcome the limitations of sparse behavioral logs (Qiao et al., 14 Nov 2024, Wang et al., 15 Jul 2025).
• Controlling Granularity and Redundancy: Attention-based regularization, profile updaters, and contrastive learning modules dynamically calibrate the number and size of interest clusters, preventing overfitting and excessive token growth (Wang et al., 15 Jul 2025, Bang et al., 20 Feb 2025).
• Mitigating Hallucination and Overfitting: Use of proven, production-quality insight extraction engines; redundancy elimination and ranking for insights; controlled output space (predefined clusters); and explicit offline precomputation (Ma et al., 2023, Wang et al., 25 May 2024).
• System Update and Responsiveness: Balancing computationally expensive fine-tuning with low-cost, frequent RAG updates preserves both long-term model adaptation and agility for dynamic trends (Meng et al., 23 Oct 2025).
• Ethical, Safety, and Transparency Considerations: Explicit alignment with user feedback, journey explanation facilities, transparency in multi-agent frameworks, and statistical fairness audits (Christakopoulou et al., 2023, Peng et al., 31 Oct 2024, Deventer et al., 26 Dec 2024).
• Real-Time Scalability: Lookup-table based precomputed transition mapping and decoupling online/offline computation demonstrate practical feasibility for large-scale production deployment (Wang et al., 7 Apr 2025, Meng et al., 23 Oct 2025).

7. Summary Table: Representative Architectures and Their Components

System	LLM Role	User Modeling Unit	Adaptation/Update	Key Evaluation/Result
InsightPilot	Reasoning, sequence	IQuery, AE, Insight	Action chain loop	Outperforms baselines in completeness/relevance
Journey Extraction	Naming, clustering	Interest Journey	Few-shot, prompt tuning	BLEURT/SacreBLEU; 25.3% journey-aligned candidates
Hierarchical Hybrid	Cluster generator	Interest cluster	Fine-tune + RAG	Increased UCI@N, user engagement
PURE	Profile summarizer	Likes/dislikes/features	Profile updater	Manages token length, sustains N@k over time
SLEGO	Microservice rec	Pipeline config	Embedding + LLM prompt	Modular analytics, 2-step rec, collaborative adap.
Dual-Level Multi-Interest	Cluster, alignment	Semantic/collab clusters	Capsule + LLM align	Top results on Recall/NDCG/HR

The field demonstrates ongoing innovation addressing core challenges of dynamic user interest adaptation, semantic transparency, and scalable, controllable reasoning underpinned by LLMs and hybrid architectures.