Reasearch on Cross-National E-commerce User Behavior Analysis and Conversion Rate Improvement Based on the Improved XLSTM Algorithm
oraz
17 mar 2025
O artykule
Data publikacji: 17 mar 2025
Otrzymano: 02 lis 2024
Przyjęty: 18 lut 2025
DOI: https://doi.org/10.2478/amns-2025-0828
Słowa kluczowe
© 2025 Jingbo Zhai et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2-3.
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Figure. 5.
Device and Platform Distribution
| Device Type | Frequency | Conversion Rate (%) |
|---|---|---|
| Mobile | 44,392 | 5.2 |
| Desktop | 25,670 | 7.8 |
| Tablet | 11,582 | 4.1 |
Performance Comparison of Clustering Algorithms
| Algorithm | Silhouette Score | Davies-Bouldin Index (DBI) | Adjusted Rand Index (ARI) |
|---|---|---|---|
| K-means Clustering | 0.62 | 1.09 | 0.71 |
| DBSCAN | 0.58 | 1.32 | 0.67 |
| Agglomerative Hierarchical | 0.55 | 1.45 | 0.64 |
| XLSTM + K-means Clustering | 0.77 | 0.92 | 0.81 |
User Purchase Behavior
| User Segment | Average Purchase Frequency | Avg. Purchase Value | Conversion Rate (%) |
|---|---|---|---|
| Frequent Shoppers | 5/month | $157 | 25% |
| Occasional Shoppers | 2/month | $75 | 8% |
| Window Shoppers | 0.5/month | $21 | 1% |