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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".