Prediction of Purchase Intention among E-Commerce Platform Users Based on Big Data Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The boom of e-commerce platforms (ECPs) has created a massive amount of data on user behaviors. To realize precision marketing, the ECPs must mine out the effective information from the massive data, and predict the purchase intention of their users. Therefore, this paper attempts to design an effective prediction model of purchase intention among ECP users. Firstly, feature engineering, coupled with big data analysis, was performed to identify the features that directly bear on the purchase intention of ECP users. Drawing on these features, two prediction models were established based on linear regression (LR) and extreme gradient boosting (XGBoost), respectively. The XGBoost model was found to be more effective through experiment on ECP users using cellphones. Finally, the prediction effects of the XGBoost-based prediction model were verified through an experiment on Epinions Trust Network Dataset. The research results provide new insights into user behaviors on ECPs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it