Data-driven prediction of future purchase behavior in cross-border e-commerce using sequence modeling with PSO-tuned LSTM
Yang Yang, Najmul Hasan, Najmul Hasan, Najmul Hasan, Najmul Hasan, Najmul Hasan · 2025
Read Data-driven prediction of future purchase behavior in cross-border e-commerce using sequence modeling with PSO-tuned LSTM by Yang Yang, Najmul Hasan, Najmul Hasan, Najmul Hasan, Najmul Hasan, Najmul Hasan (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12799269.txt.
Description
Yang Yang presents a 2025 study on forecasting what shoppers will buy next in cross border online trade. Platforms gain from sharp forecasts of user buys, for both smooth operations and user experience. Buying paths unfold over time, with long gaps and noisy signals that strain plain models.
The framework first splits the behavior series into clean wave parts to cut noise and draw out multi rate traits. Those parts feed a memory network that learns long span links in the data. A swarm search method tunes the network settings on its own, which curbs overfit and lifts reach to new users. Tests showed the hybrid beat standard methods in accuracy and steadiness.
Catalog record
| CREATOR | Yang Yang, Najmul Hasan, Najmul Hasan, Najmul Hasan, Najmul Hasan, Najmul Hasan |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 76 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |