Aspect-Aware Sentiment Interaction Modeling with DeBERTa for Enhanced Review-Based Recommendations
Bibliographic record
Abstract
Nowadays, recommender systems play a very important role in our daily lives and in influencing decision-making processes. They serve as key tools for mitigating information overload across many platforms. Their ability to narrow down options based on individual preferences not only helps users by saving time and effort but also enables many businesses to boost user engagement. However, despite substantial progress in recent years, there remains great potential to further enhance performance by leveraging aspect-level sentiment signals embedded in user reviews. In this paper, we propose a novel aspect-sentiment-aware recommender system that incorporates both explicit and implicit interaction modeling between user and item aspect-level sentiment profiles. These profiles are built using Decoding-enhanced BERT with Disentangled Attention (DeBERTa) to extract fine-grained sentiment information across domain-specific aspects, enabling the model to capture user preferences from past reviews and item attributes from collective feedback. Experiments on benchmark datasets show that our model outperforms several state-of-the-art review-based recommender systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".