Deep Learning-based Hotel Customer Behavior Prediction Model Construction and Management Strategy Optimization
Bibliographic record
Abstract
Accurately capturing the behavioral factors of different types of customer groups and adopting targeted service strategies is the key to business competition in the hotel industry.In this paper, we combine the variance Boston matrix and PSO-based K-means algorithm to achieve hotel customer attribute segmentation based on customer behavior, customer value and word-of-mouth reliability, and then use deep learning algorithms to construct a hotel customer behavior prediction model.The feature fusion layer and SENet are incorporated into the residual network in order to utilize the feature expression ability of different layers and the spatial coding ability between different channels to enhance the hotel customer behavior predictive ability.Downloading the public dataset from the online wine travel platform for example analysis, it is found that the classification of this paper's algorithm before customer segmentation has a correct rate of 83.75%, which is higher than the rest of the baseline models.After customer segmentation this paper's algorithm achieves the highest recall rate in all customer categories, and the recall rate is as high as 84% on category 1 customer groups, and the superiority of the designed algorithm is verified.This study facilitates hotel management to target customer service and retention according to different customer groups.
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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 | 0.001 |
| 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 | 0.000 |
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 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".