Machine learning-based e-commerce platform repurchase customer prediction model.
In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges o...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0243105&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850187727938519040 |
|---|---|
| author | Cheng-Ju Liu Tien-Shou Huang Ping-Tsan Ho Ping-Tsan Ho Jui-Chan Huang Ching-Tang Hsieh |
| author_facet | Cheng-Ju Liu Tien-Shou Huang Ping-Tsan Ho Ping-Tsan Ho Jui-Chan Huang Ching-Tang Hsieh |
| author_sort | Cheng-Ju Liu |
| collection | DOAJ |
| description | In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges of traditional online shopping behavior prediction methods, and proposes an online shopping behavior analysis and prediction system. The paper chooses linear model logistic regression and decision tree based XGBoost model. After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. The purpose is to avoid the accuracy of the linear model easy to fit and the decision tree model over-fitting. The results show that the model constructed by the article has further improvement than the single model. Finally, through two sets of contrast experiments, it is proved that the algorithm selected in this paper can effectively filter the features, which simplifies the complexity of the model to a certain extent and improves the classification accuracy of machine learning. The XGBoost hybrid model based on p/n samples is simpler than a single model. Machine learning models are not easily over-fitting and therefore more robust. |
| format | Article |
| id | doaj-art-ba3312f577a9466f964017d80417bd7c |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ba3312f577a9466f964017d80417bd7c2025-08-20T02:16:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011512e024310510.1371/journal.pone.0243105Machine learning-based e-commerce platform repurchase customer prediction model.Cheng-Ju LiuTien-Shou HuangPing-Tsan HoPing-Tsan HoJui-Chan HuangChing-Tang HsiehIn recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges of traditional online shopping behavior prediction methods, and proposes an online shopping behavior analysis and prediction system. The paper chooses linear model logistic regression and decision tree based XGBoost model. After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. The purpose is to avoid the accuracy of the linear model easy to fit and the decision tree model over-fitting. The results show that the model constructed by the article has further improvement than the single model. Finally, through two sets of contrast experiments, it is proved that the algorithm selected in this paper can effectively filter the features, which simplifies the complexity of the model to a certain extent and improves the classification accuracy of machine learning. The XGBoost hybrid model based on p/n samples is simpler than a single model. Machine learning models are not easily over-fitting and therefore more robust.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0243105&type=printable |
| spellingShingle | Cheng-Ju Liu Tien-Shou Huang Ping-Tsan Ho Ping-Tsan Ho Jui-Chan Huang Ching-Tang Hsieh Machine learning-based e-commerce platform repurchase customer prediction model. PLoS ONE |
| title | Machine learning-based e-commerce platform repurchase customer prediction model. |
| title_full | Machine learning-based e-commerce platform repurchase customer prediction model. |
| title_fullStr | Machine learning-based e-commerce platform repurchase customer prediction model. |
| title_full_unstemmed | Machine learning-based e-commerce platform repurchase customer prediction model. |
| title_short | Machine learning-based e-commerce platform repurchase customer prediction model. |
| title_sort | machine learning based e commerce platform repurchase customer prediction model |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0243105&type=printable |
| work_keys_str_mv | AT chengjuliu machinelearningbasedecommerceplatformrepurchasecustomerpredictionmodel AT tienshouhuang machinelearningbasedecommerceplatformrepurchasecustomerpredictionmodel AT pingtsanho machinelearningbasedecommerceplatformrepurchasecustomerpredictionmodel AT pingtsanho machinelearningbasedecommerceplatformrepurchasecustomerpredictionmodel AT juichanhuang machinelearningbasedecommerceplatformrepurchasecustomerpredictionmodel AT chingtanghsieh machinelearningbasedecommerceplatformrepurchasecustomerpredictionmodel |