A User Behavior Prediction Method for Web Applications Based on Deep Forest
DOI:
https://doi.org/10.13052/jwe1540-9589.2412Keywords:
Deep forest (DF), web application, user behavior, predictionAbstract
To increase the sales of agricultural products in e-commerce, understanding customer preferences is essential. In agricultural web applications, data mining techniques can help farmers analyze customer behavior patterns and identify preferences, thus optimizing product design or offering more precise personalized services, which, in turn, can enhance farmers’ decision-making in agricultural production. This study proposes a web application user behavior prediction method based on deep forest, which addresses the issue of traditional learning methods requiring a large number of hyperparameter settings. Analysis results show that the Mondrian deep forest model has an accuracy of 95.42% and a running time of 55 s. The accuracy and efficiency of the Mondrian deep forest model are higher than for other models, and the proposed model can improve the accuracy of predicting user behavior in web applications. The effectiveness of the algorithm has been validated through practical testing.
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References
Silahtaroglu G, Donertasli H. Analysis and prediction of E
-customers’ behavior by mining clickstream data[C]. IEEE International Conference on Big Data. IEEE, 2015: 1466–1472.
Wen Wen, Lin Zetian, Cai Ruichu, et al. User Dynamics Preference Prediction Based on Embedding Learning[J]. Computer Science, 2019, 46(10):32–38.
Hu Xiaoli, Zhang Huibing, DONG Junchao, et al. Prediction of Repeated Purchase Behavior of New Users on E-commerce Platform Based on Ensemble Learning[J]. Modern Electronics, 2020, 43(11):115–119.
Strub F, Mary J. Collaborative filtering with stacked denoisingautoencoders and sparse inputs[C]∥
NIPS workshop on machine learning for ecommerce, 2015.
Xu Z, Chen C, Lukasiewicz T, et al. Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling[C]∥
Proceedings of the 2 5 thACM International on Conference on Information and Knowledge Management. ACM, 2016:1921–1924.
Wei J, He J, Chen K, et al. Collaborative filtering and deep learning based recommendation system for cold start items[J]. Expert Systems with Applications, 20 17, 69:29–39.
Huang L W, Jiang B T, Lv S Y, et al. Survey on Deep Learning Based Recommender Systems[J]. Chinese Journal of Computers, 2018, 42(7):191–219.
Zeng X Y, Liu Q, Zhao H K, et al. Online Consumptions Prediction via Modeling User Behaviors and Choices[J]. Journal of Computer Research and Development, 2016, 53(8):1673–1683.
Ding Z, Qin Z, Zheng W T, et al. A Recommendation Model Based on Browsing Behaviors of Mobile Users [J]. Journal of University of Electronic Science and Technology of China, 2017, 46(6):907–912.
Timothy Dube, Mbulisi Sibanda, Onisimo Mutanga. Fine-scale characterization of irrigated and rainfed croplands at national scale using multi-source data, random forest, and deep learning algorithms, ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 204:117–130.
Praveen Modi, Yugal Kumar. Smart detection and diagnosis of diabetic retinopathy using bat based feature selection algorithm and deep forest technique, Computers & Industrial Engineering, 2023, 182:109364.
Olsina, L., Garrido, A., Rossi, G., Distante, D., and Canfora, G. Web Application Evaluation And Refactoring: a Qualityoriented Improvement Approach. Journal of Web Engineering, 2008, 7(4): 258–280.
Nayak, A., Božić, B., and Longo, L. Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach. Journal of Web Engineering, 2023, 22(01), 175–196.
Ashley Scillitoe, Pranay Seshadri, Mark Girolami, Uncertainty quantification for data-driven turbulence modelling with Mondrian forests, Journal of Computational Physics, 2021, 430:110116.
Wang Ying, Ruan Mengli. Simulation of risk assessment of abnormal transactions in e-commerce based on big data[J]. Computer Simulation, 2018, 35(3):369–372.
Li Xiaolei, Wang Yingtao, Xu Guowei, et al. Information Accurate Delivery System Based on User Access Trajectory[J]. Information Technology, 2020, 44(7):58–61.
Hu Xiaoli, Zhang Huibing, Dong Junchao, et al. Prediction of Repeated Purchase Behavior of New Users on E-commerce Platform Based on Ensemble Learning[J]. Modern Electronics, 2020, 43(11):115–119.
Li Zhengfang, Du Jinglin, Zhou Yun. Rainfall Prediction Model Based on Improved CART Algorithm[J]. modern electronic technology, 2020, 43(2):133–137.
Xue Canguan, Yan Xuefeng. Software Defect Prediction Based on Improved Deep Forest Algorithm[J]. Computer Science, 2018, 45(8):160–165.
Ren Jie, Hou Bojian, Jiang Yuan. Deep forest architecture based on multi-example learning[J]. Computer Research and Development, 2019, 56(8):1670–1676.
