Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives
Bibliographic record
Abstract
Recommender systems play a pivotal role in helping users navigate a vast selection of products and services. On online platforms, users have the opportunity to share feedback in various modes, such as numerical ratings, textual reviews, and likes/dislikes. Traditional recommendation systems rely on users’ explicit ratings or implicit interactions (e.g., likes, clicks, shares, and saves) to learn user preferences and item characteristics. Beyond numerical ratings, textual reviews provide insights into users’ fine-grained preferences and item features. Analyzing these reviews is crucial for enhancing the performance and explainability of personalized recommendation results. In this article, we provide a comprehensive overview of the development in review-based recommender systems over recent years, highlighting the importance of reviews in recommender systems, as well as the challenges associated with extracting features from reviews and integrating them into ratings. Specifically, we introduce a classification scheme in terms of both the integration of reviews into recommendation systems and the technical methodology. Additionally, we summarize the state-of-the-art methods, analyzing their unique features, effectiveness, and limitations. The study also presents the various evaluation metrics, comparative analysis, datasets, and real-world applications of review-based recommendation systems. Finally, we propose potential directions for future research, including multi-modal data integration, multi-criteria rating information, and ethical considerations.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".