A Multi-Model Approach for User Portrait
Age, gender, educational background, and so on are the most basic attributes for identifying and portraying users. It is also possible to conduct in-depth mining analysis and high-level predictions based on such attributes to learn users’ preferences and personalities so as to enhance users’ online...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/13/6/147 |
| _version_ | 1797531834054082560 |
|---|---|
| author | Yanbo Chen Jingsha He Wei Wei Nafei Zhu Cong Yu |
| author_facet | Yanbo Chen Jingsha He Wei Wei Nafei Zhu Cong Yu |
| author_sort | Yanbo Chen |
| collection | DOAJ |
| description | Age, gender, educational background, and so on are the most basic attributes for identifying and portraying users. It is also possible to conduct in-depth mining analysis and high-level predictions based on such attributes to learn users’ preferences and personalities so as to enhance users’ online experience and to realize personalized services in real applications. In this paper, we propose using classification algorithms in machine learning to predict users’ demographic attributes, such as gender, age, and educational background, based on one month of data collected with the Sogou search engine with the goal of making user portraits. A multi-model approach using the fusion algorithms is adopted and hereby described in the paper. The proposed model is a two-stage structure using one month of data with demographic labels as the training data. The first stage of the structure is based on traditional machine learning models and neural network models, whereas the second one is a combination of the models from the first stage. Experimental results show that our proposed multi-model method can achieve more accurate results than the single-model methods in predicting user attributes. The proposed approach also has stronger generalization ability in predicting users’ demographic attributes, making it more adequate to profile users. |
| first_indexed | 2024-03-10T10:49:27Z |
| format | Article |
| id | doaj.art-0f18743de77945898e20672044914c03 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T10:49:27Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-0f18743de77945898e20672044914c032023-11-21T22:17:09ZengMDPI AGFuture Internet1999-59032021-05-0113614710.3390/fi13060147A Multi-Model Approach for User PortraitYanbo Chen0Jingsha He1Wei Wei2Nafei Zhu3Cong Yu4Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaAge, gender, educational background, and so on are the most basic attributes for identifying and portraying users. It is also possible to conduct in-depth mining analysis and high-level predictions based on such attributes to learn users’ preferences and personalities so as to enhance users’ online experience and to realize personalized services in real applications. In this paper, we propose using classification algorithms in machine learning to predict users’ demographic attributes, such as gender, age, and educational background, based on one month of data collected with the Sogou search engine with the goal of making user portraits. A multi-model approach using the fusion algorithms is adopted and hereby described in the paper. The proposed model is a two-stage structure using one month of data with demographic labels as the training data. The first stage of the structure is based on traditional machine learning models and neural network models, whereas the second one is a combination of the models from the first stage. Experimental results show that our proposed multi-model method can achieve more accurate results than the single-model methods in predicting user attributes. The proposed approach also has stronger generalization ability in predicting users’ demographic attributes, making it more adequate to profile users.https://www.mdpi.com/1999-5903/13/6/147user portraitmachine learningmulti-model ensemble |
| spellingShingle | Yanbo Chen Jingsha He Wei Wei Nafei Zhu Cong Yu A Multi-Model Approach for User Portrait Future Internet user portrait machine learning multi-model ensemble |
| title | A Multi-Model Approach for User Portrait |
| title_full | A Multi-Model Approach for User Portrait |
| title_fullStr | A Multi-Model Approach for User Portrait |
| title_full_unstemmed | A Multi-Model Approach for User Portrait |
| title_short | A Multi-Model Approach for User Portrait |
| title_sort | multi model approach for user portrait |
| topic | user portrait machine learning multi-model ensemble |
| url | https://www.mdpi.com/1999-5903/13/6/147 |
| work_keys_str_mv | AT yanbochen amultimodelapproachforuserportrait AT jingshahe amultimodelapproachforuserportrait AT weiwei amultimodelapproachforuserportrait AT nafeizhu amultimodelapproachforuserportrait AT congyu amultimodelapproachforuserportrait AT yanbochen multimodelapproachforuserportrait AT jingshahe multimodelapproachforuserportrait AT weiwei multimodelapproachforuserportrait AT nafeizhu multimodelapproachforuserportrait AT congyu multimodelapproachforuserportrait |