Unscrambling Customer Recommendations: A Novel LSTM Ensemble Approach in Airline Recommendation Prediction Using Online Reviews
Bibliographic record
Abstract
Customer feedback is an essential criterion for upcoming customers to learn from their experience with a company’s products. Customer reviews and ratings also help companies improve performance and figure out new methodologies to provide better services. This research concentrates on customer reviews and ratings to investigate which product a customer evaluates and its association with its recommendations. This work predicts the user recommendations in two modules. The first module performs sentiment analysis of customer reviews using the long short-term memory (LSTM) model, which estimates the probability of the customer’s sentiment about the airline’s services. The second module experimented over only various service aspect ratings on different airline services provided by customers. These two modules ensemble together to determine the predictive recommendations of the airlines. The obtained results reinforce the essential theoretical contribution to the literature on service appraisal, online review, and recommendations. In addition, our proposed ensemble approach will be helpful to those practitioners who wish to use any proposal that will provide a quick and essential vision by bringing together customer-generated reviews and ratings, thereby helping them in strategy designing, service improvement, and post-purchases planning. Also, forthcoming travelers may benefit from this proposed approach by assimilating an aggregating view of service quality.
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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 | 0.003 |
| 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) | 0.001 | 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".