A Multi-Model Approach for User Portrait

Yanbo Chen, Jingsha He, Wei Wei, Nafei Zhu and Cong Yu
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Yanbo Chen: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Jingsha He: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Wei Wei: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Nafei Zhu: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Cong Yu: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Future Internet, 2021, vol. 13, issue 6, 1-14

Abstract: 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.

Keywords: user portrait; machine learning; multi-model ensemble (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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