Abstract
The user’s age and gender play a vital role within the user portrait. In view of the lack of basic attribute information, such as the age and gender of users, this paper constructs an attribute prediction method based on stacking multimodel integration. The user’s browsing and clicking history is analyzed to predict the user’s basic attributes. First, LR, RF, XGBoost, and ExtraTree were selected as the base classifiers for the first layer of the stacking framework, and the training results of the first layer were input as new training data into the second layer LightGBM for training. Experiments show that the proposed model can improve the accuracy of prediction results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hu, J., Zeng, H.-J., Li, H., Niu, C., Chen, Z.: Demographic prediction based on user’s browsing behavior. In: Proceedings of the 16th International Conference on World Wide Web, pp. 151–160 (2007)
De Bock, K., Van den Poel, D.: Predicting website audience demographics for web advertising targeting using multiwebsite clickstream data. Fund. Inform. 98(1), 49–70 (2010)
Lu, X.: Research and analysis of user attribute inference of Chinese social networking websites based on WEB. Ph.D. thesis, Nanjing University of Aeronautics and Astronautics (2017)
Liu, B.Q., Niu, Y.: Gender recognition of chinese microblog users based on emotion characteristics. Compt. Eng. Sci. 38(9), 7 (2016)
Wang, M., Cao, Q., Sun, J., Zhang, Q., Xu, F.: A user based attribute prediction method based on ensemble learning. Small Microcomput. Syst. 41(12), 7 (2020)
Gao, J., Zhang, T., Cheng, X., Jian, G.: An age and gender prediction method based on lightGBM machine learning algorithm. Post Telecommun. Des. Technol. 9, 4 (2019)
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Yun, J., Sun, G., Chen, Q., Zhang, M., Zhu, H., Rehman, M.U.: A model combining convolutional neural network and lightGBM algorithm for ultra-short-term wind power forecasting. IEEE Access 7, 28309–28318 (2019)
Pan, B.: Application of XGBoost algorithm in hourly PM2.5 concentration prediction. IOP Conf. Ser. Earth Environ. Sci. 113, 012127 (2018)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: The 22nd ACM SIGKDD International Conference (2016)
Yin, Q.: Research on intelligent stock selection in biomedical sector based on stacking fusion model. Ph.D. thesis, Shanghai Normal University (2020)
Zhang, J., Huo, J.: Research on purchasing behavior prediction based on stacking model integration. Shanghai Manage. Sci. 43(1), 12–19 (2021)
Xu, G., Shen, Y.: Multiclassification detection method for malicious programs based on XGBoost and stacking model. Inf. Netw. Secur. 21(6), 11 (2021)
Wang, H.: Research on old vehicle valuation model based on characteristic price theory and CatBoost. Ph.D. thesis, Tianjin University of Commerce (2019)
Hang, Q., Jinghui, Y.: Application status of machine learning stochastic forest algorithm. Electron. Technol. Softw. Eng. 24, 3 (2018)
Pu, Y., Xingfu, L.: Application of stacking ensemble learning in sales forecasting. Softw. Guide 21(4), 6 (2022)
Yangheran, P., Junling, R.: An integrated detection method of malicious web pages based on stacking. Comput. Appl. 39(4), 8 (2019)
Xuemei, H., Ying, X., Huifeng, J.: Breast cancer prediction based on penalized logistic regression. Data Collect. Process. 36(6), 13 (2021)
Jiaqing, W., Zhe, W., Taipeng, Z., Jinyu, Y., Xin, Z., Yunxiang, M.: Research on breast cancer prediction model based on random forest algorithm. Chin. Med. Equip. 19, 119–123 (2022)
Aihong, W.: Research on the prediction of railway passenger refund rate based on XGBoost algorithm. Railw. J. 41(12), 7 (2019)
Hongyan, H., Guoyan, H., Bing, Z., Damiao, J.: Anomaly detection model based on limit tree feature recursive elimination and lightGBM. Inf. Netw. Secur. 1, 8 (2022)
Tong, G., Guoliang, X., Wanlin, L., Jiahao, L.: An intelligent evaluation model for housing prices based on integrating lightGBM and Bayesian optimization strategies. Comput. Appl. 40(9), 6 (2020)
Xuan, W., Xiangwei, L.: A classification model that integrates feature selection algorithms and lightGBM fusion. Fujian Comput. 38(4), 4 (2022)
Cong, Z.: Research on MOOC dropout prediction based on feature engineering. Ph.D. thesis, Jiangxi University of Finance and Economics (2020)
Wei, Y., Fengli, Y., Jing, J., Meng, Y.: A method for online parameter tuning of models based on improved logistic regression algorithm. Commun. Technol. 53(8), 5 (2020)
Acknowledgment
This work is supported by Hainan Province Science and Technology Special Fund, which is Research and Application of Intelligent Recommendation Technology Based on Knowledge Graph and User Portrait (No. ZDYF2020039). Thanks to Professor CaiMao Li, the correspondent of this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, Q., Li, C., Lin, H., Li, H., Hou, Y. (2022). User Attribute Prediction Method Based on Stacking Multimodel Fusion. In: Wang, Y., Zhu, G., Han, Q., Zhang, L., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1629. Springer, Singapore. https://doi.org/10.1007/978-981-19-5209-8_12
Download citation
DOI: https://doi.org/10.1007/978-981-19-5209-8_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5208-1
Online ISBN: 978-981-19-5209-8
eBook Packages: Computer ScienceComputer Science (R0)