MDBIF: A Multi-Dimensional Feature and Boosting Integration Framework for O2O Coupon Redemption Prediction
Bibliographic record
Abstract
The low redemption rate of coupons in Online-to-Offline (O2O) platforms poses a key challenge for marketing efficiency. To address this, we propose a Multi-Dimensional Feature and Boosting Integration Framework (MDBIF) that captures user, merchant, coupon, and interaction behaviors across seven feature groups with 26 new features. Using mutual information for feature selection and comparing XGBoost, LightGBM, and CatBoost, our framework enhances prediction robustness via data fusion and tuning. Experiments on a real-world Alibaba Tmall dataset (1.75M records) show that LightGBM achieves the best performance (AUC 0.9961, accuracy 0.9815). Key features such as user-specific coupon receipt frequency and merchant distance prove critical. Based on this, we offer actionable targeting strategies to improve O2O coupon effectiveness. Our approach provides a scalable solution for precision marketing in O2O ecosystems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".