Automatic Extraction of Theme-based Recommendation Reasons: Framework, Model Selection, Data Augmentation, and Optimization

Background : With increasing consumer rationality, high‑quality, concise, and information‑rich content is crucial for product recommendation, especially in travel scenarios where labeling POIs with multiple dimensions aids both platform recommendation and user understanding. The core challenge is automatic extraction of theme‑based recommendation reasons that satisfy both relevance and quality criteria.

Challenges : (1) Large and dynamic label space (hundreds of tags); (2) Noisy, heterogeneous user‑generated data; (3) Difficulty obtaining stable supervised data due to subjective quality assessment.

Framework : A three‑stage pipeline—Recall, Coarse‑ranking, and Fine‑ranking—combines tag‑keyword recall, sentiment and quality filters, lexical analysis, unsupervised matching for coarse filtering, and a theme‑matching model with honor‑degree weighting for fine ranking (see Figure 2).

Model Selection : Due to the massive, imbalanced label set, a matching model (interactive) is chosen over traditional classification. Interactive models (e.g., ARC‑II, MatchPyramid, ESIM) capture pairwise semantic similarity and are robust to label imbalance, while classification suffers from over‑fitting and requires retraining for new tags.

Data Augmentation : Two strategies are employed—rule‑based augmentation (targeting high‑frequency, low‑coverage tags) and back‑translation (multi‑round machine translation) to increase diversity while preserving semantics. Quality control uses syntactic analysis, honor‑degree patterns, and unsupervised semantic matching, achieving ~93% usable rate for augmented data (see Figure 3).

Model Optimization : (1) Introducing priors either at the input layer (concatenating prior knowledge) or task layer (attention or similarity matrix) improves thematic focus; (2) Transfer learning leverages pretrained language models, with careful seed selection (best random seeds improve fine‑tuning stability); (3) Replacing BERTAdam with standard Adam yields 5‑10% higher F1; (4) Layer‑wise learning rates mitigate under‑/over‑fitting between task‑specific and pretrained layers.

Conclusion : The proposed framework, matching‑model‑centric approach, and augmentation/optimization techniques effectively model both thematic relevance and content quality under limited supervised data, while future work includes hierarchical label priors via graph networks and multi‑stage fine‑tuning.