Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms
Abstract With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-10-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13638-020-01800-7 |
| _version_ | 1819207903986319360 |
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| author | Huibing Zhang Junchao Dong |
| author_facet | Huibing Zhang Junchao Dong |
| author_sort | Huibing Zhang |
| collection | DOAJ |
| description | Abstract With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate. |
| first_indexed | 2024-12-23T05:30:54Z |
| format | Article |
| id | doaj.art-0fd9da4840634cd493d2f84a9fd56d9c |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-12-23T05:30:54Z |
| publishDate | 2020-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-0fd9da4840634cd493d2f84a9fd56d9c2022-12-21T17:58:29ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992020-10-012020112610.1186/s13638-020-01800-7Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platformsHuibing Zhang0Junchao Dong1Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Trusted Software, Guilin University of Electronic TechnologyAbstract With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.http://link.springer.com/article/10.1186/s13638-020-01800-7Wireless communication networkM-CommerceEnsemble learningXGBoost-logisticsLightGBM-L2Cascaded deep forest |
| spellingShingle | Huibing Zhang Junchao Dong Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms EURASIP Journal on Wireless Communications and Networking Wireless communication network M-Commerce Ensemble learning XGBoost-logistics LightGBM-L2 Cascaded deep forest |
| title | Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms |
| title_full | Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms |
| title_fullStr | Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms |
| title_full_unstemmed | Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms |
| title_short | Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms |
| title_sort | application of sample balance based multi perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms |
| topic | Wireless communication network M-Commerce Ensemble learning XGBoost-logistics LightGBM-L2 Cascaded deep forest |
| url | http://link.springer.com/article/10.1186/s13638-020-01800-7 |
| work_keys_str_mv | AT huibingzhang applicationofsamplebalancebasedmultiperspectivefeatureensemblelearningforpredictionofuserpurchasingbehaviorsonmobilewirelessnetworkplatforms AT junchaodong applicationofsamplebalancebasedmultiperspectivefeatureensemblelearningforpredictionofuserpurchasingbehaviorsonmobilewirelessnetworkplatforms |