Expertise-Based Personalization

Updated 5 December 2025

Expertise-based personalization is an approach that tailors content, navigation, and visualization by modeling or detecting a user's proficiency using quantitative interaction data and domain signals.
It leverages techniques such as reinforcement learning, inverse reinforcement learning, hierarchical drillboards, and expert-aware embedding to adjust system responses in real time.
Empirical evaluations show significant improvements in usability, click-through rates, and decision support efficiency across adaptive navigation, search, and recommendation systems.

Expertise-based personalization denotes the adaptation of system content, presentation, or guidance mechanisms to the inferred or stated expertise level of individual users. Central to this paradigm is the explicit modeling or automatic detection of user proficiency, interest, or domain-specific knowledge, enabling differentiated user experiences in navigation, recommendation, decision support, and visualization. Contemporary approaches span predictive modeling, user-state inference, apprenticeship learning, matrix factorization, hierarchical information organization, and passive or active tailoring, with demonstrable impact on usability, accuracy, and engagement across diverse contexts.

1. Formal Modeling and Detection of User Expertise

Expertise estimation typically relies on quantitative features derived from user interactions, profiles, or prior behavior. For example, enterprise AI assistants classify queries into expertise levels (novice, intermediate, expert) employing LLM–based classifiers applied to lexical, syntactic, and domain-specific signal vectors, such as formulaic ratios, jargon density, and readability scores (Siyan et al., 28 Nov 2025). In adaptive navigation, user expertise is represented as individual dynamics models over a Markov Decision Process (MDP), with personalization effected via convex combinations of pre-trained expert trajectories and online adaptation to user behaviors (Ohn-Bar et al., 2018).

Hierarchical visualization tools, such as drillboards, assign abstraction labels to visualization components (e.g., “unit,” “novice,” “expert,” “intermediate”) attached at each node in their representation trees, providing users with entry points and drill-down pathways aligned to their expertise (Shin et al., 2024). In recommender systems, expert-based personalization spans utility recovery via inverse reinforcement learning from demonstrated expert actions (Lechiakh et al., 2021), and integration of expert IDs as embedding vectors for personalized encoding in transformer architectures (Peng et al., 2023). Expertise search platforms, such as LinkedIn, model expertise as membership in sparse skill–member matrices and apply matrix factorization to infer latent expertise vectors (Ha-Thuc et al., 2016).

2. Personalization Methods and System Architectures

Personalization flows from expertise modeling to system response via varied methodologies:

Hierarchical Drillboards: Construct adaptive dashboards by recursively merging chart “atoms” using six merge operators (labeling, summarizing, archetype selection, projection, juxtaposition, overlay), enabling users to select views matched to their expertise level (Shin et al., 2024). The authoring environment (DrillVis) tags leaf nodes as “expert” and root nodes as “novice,” allowing custom intermediate abstraction levels.
Reinforcement and Apprenticeship Learning: Systems such as PING apply model-based RL, maintaining weighted ensembles of expert dynamics models and rapidly adapting to new users through SGD-based updates on combination weights. FEBR recovers expert utility via Maximum Entropy IRL and then provides recommendations by matching user interest-state vectors to expert states, utilizing nearest-neighbor classifiers for slate selection (Lechiakh et al., 2021, Ohn-Bar et al., 2018).
Expertise-Aware Embedding and Ranking: PEPT prepends expert-ID soft-prompt embeddings and vote-score representations to transformer inputs, jointly training question-level masked LLMs and vote-regression heads to encode both interest and expertise, yielding personalized expert-level representations for expert finding and ranking (Peng et al., 2023).

These strategies may be passive—where the system infers and adapts without user intervention (Siyan et al., 28 Nov 2025)—or active, offering explicit user controls to override or refine expertise-based defaults.

3. Evaluation of Personalization Impact

Quantitative and qualitative studies demonstrate the effectiveness and nuanced outcomes of expertise-based personalization:

Navigation Systems: Weighted experts models converge in under two minutes of adaptation data, with a ~20% reduction in end-point prediction error and 49% fewer large path deviations compared to non-adaptive baselines (Ohn-Bar et al., 2018).
Search and Recommendation: Matrix factorization–based expertise search yields statistically significant improvements in click-through rates (CTR@1: +18–31%, CTR@10: +11–18%) and downstream user actions (messages: +20–37%) (Ha-Thuc et al., 2016). FEBR elevates recommended content quality, outperforming baseline engagement-optimizing agents while maintaining comparable watch-time (Lechiakh et al., 2021).
Visualization Dashboards: Drillboard authoring by subject-matter experts yields novice views with drastically reduced chart counts and accurately guides casual users to intended comparisons, achieving 100% question accuracy and sub–10 minute completion times in end-user evaluations (Shin et al., 2024).
AI Decision Aids: Behavioral experiments indicate that while users state a strong affective preference for combined expert–algorithmic feature selection, actual reliance on advice is domain-sensitive and unrelated to selection method; medical contexts amplify reliance, whereas personality and confidence moderate advice acceptance (Kornowicz et al., 2024).
Task-Oriented AI Assistants: Passive expertise-based personalization improves test scores for novices and experts and reduces physical/temporal workload, yet may raise mental load and frustration in time-constrained tasks (Siyan et al., 28 Nov 2025).

Tables below summarize selected empirical outcomes from key studies.

System/Study	Personalization Metric	Gain/Outcome
LinkedIn Search	CTR@1 (Recruiter)	+31%
PING Navigation	20s End-Point Error	–2.2 m (~20%)
Drillboards	User Question Accuracy	100%
PEPT Expert Finding	MRR (vs. baseline)	+1–2%
AI Assistant	Novice Test Score (Personalized)	56.0% vs 49.2% (Baseline)

4. Passive vs. Active Personalization: Limitations and Hybrid Strategies

Passive expertise framing, defined by automated inference and template-based response adaptation, facilitates context-appropriate delivery but exhibits inherent limitations. In AI-assisted test-taking, personalized content reduces task load but often increases frustration if verbosity mismatches user context (e.g., time pressure) or if expertise misclassification occurs (Siyan et al., 28 Nov 2025). Behavioral analysis reveals divergence between users' stated preferences (“affective heuristics”) and operational reliance (“behavioral reliance”), emphasizing the need to transcend static labels in favor of adaptive, explainable, and user-driven mechanisms (Kornowicz et al., 2024).

Hybrid approaches are recommended to mitigate the limitations of passive adaptation:

Expertise Sliders and Quick Toggles: Allow on-the-fly user adjustment (e.g., detail level, jargon content).
Context-Aware Response Generation: Switch modes based on explicit input or inferred task phase (study vs. exam).
Responsive Prompt Interface: Facilitate iterative refinement via click-based options (e.g., "more examples," "simplify").
Continuous Feedback Loops: Incorporate system learning from user adjustments to anticipate needs.
Robust Expertise Verification: Periodic calibration through self-assessment or additional test items.

These combined strategies enable systems to respond dynamically both to long-term user profiles and momentary situational requirements.

5. Methodological Considerations and Generalization

Across domains, expertise-based personalization integrates regression, matrix factorization, RL, IRL, transformer pre-training, and hierarchical structure construction:

Collaborative Filtering and Latent Factor Models: Efficiently impute hidden expertise scores for sparse user–skill matrices (Ha-Thuc et al., 2016).
Weighted Experts and Fine-Tuning: Rapid online adaptation through offline expert model mixing, regularization, and transfer-learning protocols, improving prediction and robustness in low-data regimes (Ohn-Bar et al., 2018).
Hierarchical Visualization Design: Formal chart vocabulary and merge operators support abstraction levels and navigation toolchains for differentiated insight (Shin et al., 2024).
Inverse Reinforcement Learning: Recovers latent expert utilities and operationalizes high-quality policy transfer in recommendation environments (Lechiakh et al., 2021).
Personalized Embedding Architectures: Embeds expert identity and vote signals into unified input spaces, supporting fine-grained matching in community question answering (Peng et al., 2023).

Generalization beyond domain-specific settings is achieved by abstracting the modeling of expertise as latent factors, controllers, or conditional vectors, with observed benefits across navigation, search, recommendation, and decision support. Key open challenges include scalability (e.g., deep IRL, metric learning for large state spaces), robustness to adversarial or misaligned expert data, and real-time calibration of expertise inference.