Preference-Guided Topic Clustering

• Preference-guided topic clustering is an approach that integrates explicit user signals into standard clustering methods to produce more meaningful and actionable topic groupings.
• It incorporates mechanisms such as pairwise constraints, seed terms, and soft assignments to refine latent topic structures and align clusters with user and domain needs.
• Empirical evidence demonstrates improved accuracy and cluster purity, with methods like CATCH PeC and Guided NMF outperforming conventional techniques in diverse applications.

Preference-guided topic clustering refers to a class of unsupervised or semi-supervised methods that integrate explicit or implicit user preferences into topic clustering models, generating groupings that are more meaningful, actionable, or interpretable in user- or application-specific contexts. This is in contrast to classical topic clustering, which relies solely on intrinsic data structure, often yielding clusters misaligned with user intentions, domain requirements, or business goals.

1. Conceptual Foundations and Motivation

Standard topic clustering identifies latent group structure (e.g., topics, themes, aspects) in unstructured data such as documents, utterances, or social media posts. However, the "natural" clusters harvested by generic algorithms (e.g., KMeans, LDA, NMF) may not reflect the distinctions or aggregations most relevant to a user or downstream process. Preference-guided approaches correct this by directly incorporating external signals—must-link/cannot-link constraints, seed terms, pairwise feedback, or more general preference annotations—into model construction and inference.

This paradigm is motivated by:

• The need for alignment across heterogeneous or ambiguous datasets (e.g., cross-domain dialogue, multilingual corpora).
• Reducing supervision cost by focusing limited annotation on high-leverage constraints or feedback.
• Supporting personalized, domain-adapted, or business-critical topics not captured by distributions alone.

2. Mathematical Formulations and Optimization Objectives

Preference-guided clustering algorithms augment standard objectives by embedding user signals into similarity, assignment, or generative factorization processes. Distinct formulations include:

• Semantic-preference fusion kernels: As in the PeC module of CATCH (Ke et al., 25 Dec 2025), user should-link/cannot-link feedback is encoded as a preference weight $w_{x,y}$, fused with semantic distance $d_{\mathrm{sem}}(x,y)$ via $$d_{\mathrm{SP}}(x,y) = w_{x,y} \cdot d_{\mathrm{sem}}(x,y)$$. Clustering then minimizes intra-cluster $d_{\mathrm{SP}}$.
• Supervision-augmented factorization: Guided NMF (Vendrow et al., 2020) minimizes $\|X - WH\|_F^2 + \lambda\|S - WG\|_F^2$, where $S$ encodes user-provided seedwords and $\lambda$ balances supervision versus data fit.
• Constrained contrastive learning: Active-deep personalized clustering (Geng et al., 2024) optimizes representation learning with loss terms that (a) bring must-link pairs together, (b) push cannot-link pairs apart, and (c) select the most informative queries for rapid adaptation.
• Generative models with explicit preference factors: In rating and review analysis, TSPRA (Chen et al., 2018) introduces latent per-topic, per-user preference variables decoupled from sentiment and topic occurrence, enabling aspect discovery aligned with user priorities via hierarchical nonparametric Bayes.

A representative optimization from CATCH:

$$\min_{C_1,\ldots,C_K}\ \sum_{k=1}^K \sum_{i,j\in C_k} D^{\mathrm{SP}}_{ij}$$

where $$D^{\mathrm{SP}}_{ij}=w_{ij}\cdot D^{\mathrm{sem}}_{ij}$$. Closed-form assignment is generally intractable, so anchor-based or iterative algorithms are used.

3. Preference Elicitation and Encoding Mechanisms

Approaches differ in the form and deployment of preference information:

• Pairwise Constraints: Explicit should-link/cannot-link pairs supplied by users, as in PeC (Ke et al., 25 Dec 2025) and active constrained deep clustering (Geng et al., 2024).
• Seed Terms or Topics: User-compiled sets of seed words mapped to desired topics (Guided NMF (Vendrow et al., 2020)), facilitating parts-based interpretability and targeted extraction.
• Soft Assignments or Multi-dimensional Profiles: User-topic preference vectors in applications such as social media, quantifying the intensity of engagement in various topics (MUM model (Recalde et al., 2018)).
• Preference Reward Models: Learnable predictors (e.g., BERT-based) that infer preference scores from limited labels, enabling generalization and efficient use of annotations (PeC in CATCH (Ke et al., 25 Dec 2025)).
• Prompt Engineering and Guided Summarization: Instruction-tuned LLMs instructed with domain knowledge and user expectations to influence topic boundaries (ClusterFusion (Xu et al., 4 Dec 2025)).

In all cases, the embedding of preference signals impacts either the representation space, the similarity metric, or the clustering criterion.

4. Representative Algorithms and Practical Pipelines

Multiple operational paradigms have been established:

• Preference-weighted Clustering (CATCH PeC) (Ke et al., 25 Dec 2025): A 4-stage routine anchors on semantic spectral clusters, reweights by PRM-inferred preferences, detects conflicts, subclusters, and reassigns, with thresholds controlling the influence of preference signals. This kernel adjustment enables the rectification of misaligned clusters post hoc.
• Active Targeted Representation Learning (PCL) (Geng et al., 2024): Batchwise contrastive learning with cross-instance attention incorporates user queries, while an active selection mechanism focuses labeling on uncertain or critical boundary pairs to maximize alignment per annotation budget, yielding guaranteed monotonic risk reduction.
• Guided NMF (Vendrow et al., 2020): Multiplicative update rules integrate data and supervision (seed topics) in factorization, allowing for interpretable, computable user-driven topic models.
• Hybrid LLM Clustering (ClusterFusion) (Xu et al., 4 Dec 2025): Combines embedding-based pre-grouping with LLM-driven topic summarization and assignment, where prompts explicitly encode user grouping and topic description preferences at multiple stages.

The table below summarizes some key algorithmic ingredients:

Method/Framework	Preference Injection Point	Primary Model/Mechanism
CATCH PeC (Ke et al., 25 Dec 2025)	Pairwise PRM, should-link/cannot-link	Spectral+PRM kernel fusion
Guided NMF (Vendrow et al., 2020)	Seedword supervision	NMF + guidance regularizer
PCL (Geng et al., 2024)	Must-link/cannot-link pairs	Deep attention + contrastive
ClusterFusion (Xu et al., 4 Dec 2025)	Prompts, embedder, assignment constraints	LLM-core hybrid

5. Empirical Evidence and Utility

Experimental validation consistently demonstrates the advantage of preference-guided methods:

• Preference-enhanced clustering (PeC) in CATCH yields +7.0 accuracy and +7.8 cosine similarity gains on dialogue theme clustering versus semantic-only baselines, confirming improved cluster purity and label quality (Ke et al., 25 Dec 2025).
• On social media user clustering, the MUM model recovers highly pure and semantically coherent clusters, outperforming classic supervised tf-idf models, especially in distinguishing activity intensity and nuanced engagement (Recalde et al., 2018).
• Guided NMF regularly surpasses seeded LDA in topic and document classification AUC, particularly under minimal supervision (Vendrow et al., 2020).
• In the review domain, TSPRA’s decoupling of preference and sentiment surfaces “critical aspects” not discoverable with sentiment alone and significantly improves rating prediction and sentiment correlation (Chen et al., 2018).
• Active preferential deep clustering (PCL) exhibits substantially higher NMI (~0.45 vs ~0.03) and accuracy in user-defined orientations with only 0.0004–0.002% of possible queries labeled, verifying preference efficiency and adaptability (Geng et al., 2024).

Ablation and sensitivity studies further indicate that omitting preference signals (e.g., “w/o-PeC” in CATCH or unsorted input orders in ClusterFusion (Xu et al., 4 Dec 2025)) can result in severe drops in accuracy and NMI, elucidating the indispensability of these signals for alignment and quality.

6. Challenges, Limitations, and Future Directions

Despite clear advantages, several open challenges and limitations persist:

• Scalability of Annotation: All methods are bounded by the availability and coverage of preference data. Active/efficient querying (as in PCL) mitigates budget requirements, but guarantees only hold for sufficient coverage of the annotation space (Geng et al., 2024).
• Seed Quality and Bias: Outcomes are sensitive to the salience and granularity of seeds or annotated pairs; misleading or redundant seeds can reduce interpretability or force improper topic overlap (Guided NMF (Vendrow et al., 2020)).
• Interpretability vs. Flexibility: Models that maximize user alignment may diverge from data-derived semantics, affecting generalizability or external validity.
• Inference Bottlenecks: Hybrid LLM pipelines (ClusterFusion (Xu et al., 4 Dec 2025)) are limited by prompt window size and the “topic summarization” step, not the assignment. Improved summary prompt design and ordering heuristics may close this gap.
• Automatic Hyperparameter Tuning: Determining $\lambda$ (guidance strength), $K$ (number of clusters), and other meta-parameters remains an area for empirical search and can affect stability and interpretability.

Directions for further research include more efficient annotation schemes, dynamic preference elicitation, integrating richer forms of preference (e.g., group, ranked, distributional), and expanding to non-textual modalities.

7. Applications and Domains of Impact

Preference-guided topic clustering has found impact across diverse areas including:

• User-centric dialogue and theme mining: Supporting context- and feedback-aligned theme extraction for personalized conversational agents and customer support (Ke et al., 25 Dec 2025).
• Social media analysis: Uncovering fine-grained user interest profiles and community structures for recommendation, cohort analysis, and opinion mining (Recalde et al., 2018).
• Review and rating analysis: Surfacing “critical aspects” in product feedback where user concern and sentiment diverge, supporting targeted business interventions (Chen et al., 2018).
• Domain-adapted text clustering: Rapidly aligning clustering outputs with task requirements in specialized datasets or industries (e.g., code review comments, software feedback) using hybrid LLM pipelines (Xu et al., 4 Dec 2025).
• Scientific and policy document summarization: Facilitating seed-driven, purpose-aligned topic decomposition for annotated corpora or legislative datasets.
• Personalized deep visual clustering: Adapting image clusterings (e.g., ImageNet, CIFAR) to user-defined object groupings with limited feedback budget (Geng et al., 2024).

Empirical evidence demonstrates that benefit is most significant in settings with ambiguous or multidimensional natural structure, where aligning outputs to operational or user-driven ontologies is essential.