Research on Prediction of User Repurchase Behavior and Marketing Strategies in Internet Marketing

[1] Shan, H., Zhang, Y., Zhuang, W., Huang, A., & Zhang, Z. (2017). User behavior-aware scheduling based on time–frequency resource conversion. IEEE Transactions on Vehicular Technology, PP(9), 1-1. Search in Google Scholar

[2] Chung, K. C., & Liang, W. J. (2020). An empirical study of social network activities via social internet of things (siot). IEEE Access, 8, 48652-48659. Search in Google Scholar

[3] Lv, Y., & Liu, Q. (2021). Value perception impact and countermeasures analysis of new energy vehicle purchase behavior based on consumer level user review big data mining. MATEC Web of Conferences, 336, 09030. Search in Google Scholar

[4] Tua, S., Huanga, X., Zhanga, Y., Ana, M., & Liuc, L. (2018). Hmm-based user behavior prediction method in heterogeneous cellular networks. International Journal of Performability Engineering, 14(9). Search in Google Scholar

[5] Feng, Chenyue, Gao, & Qin. (2016). Branding with social media: user gratifications, usage patterns, and brand message content strategies. Computers in Human Behavior. Search in Google Scholar

[6] Xin, M., Zhang, Y., Li, S., Zhou, L., & Li, W. (2017). A location-context awareness mobile services collaborative recommendation algorithm based on user behavior prediction. International Journal of Web Services Research, 14. Search in Google Scholar

[7] Li, Q., Xie, Y. F., Wu, X. H., & Xiao, Y. (2023). User behavior prediction model based on implicit links and multi-type rumor messages. Knowledge-Based Systems, 262, 110276-. Search in Google Scholar

[8] Li, W., Zhang, C., Zhou, X., & Jin, Q. (2023). Dynamic multi-view group preference learning for group behavior prediction in social networks. Expert Systems with Applications. Search in Google Scholar

[9] Liu, J., Yang, W., & Cong, L. (2022). The role of value co-creation in linking green purchase behavior and corporate social responsibility – an empirical analysis of the agri-food sector in china. Journal of Cleaner Production. Search in Google Scholar

[10] Feng, L. (2022). Data analysis and prediction modeling based on deep learning in e-commerce. Scientific programming(Pt.8), 2022. Search in Google Scholar

[11] LiuBang, ZhangHanlin, KongLinglong, & NiuDi. (2021). Factorizing historical user actions for next-day purchase prediction. ACM Transactions on the Web (TWEB). Search in Google Scholar

[12] Nguyen, M. D., & Cho, Y. S. (2019). A hybrid generative model for online user behavior prediction. IEEE Access, PP(99), 1-1. Search in Google Scholar

[13] Gan, M., & Xiao, K. (2019). R-rnn: extracting user recent behavior sequence for click-through rate prediction. IEEE Access. Search in Google Scholar

[14] Zhang, J., Wu, T., & Fan, Z. (2019). Research on precision marketing model of tourism industry based on user’s mobile behavior trajectory. Mobile Information Systems, 2019(4), 1-14. Search in Google Scholar

[15] Shim, J. P., & Taylor, R. (2019). Purchase-based analytics and big data for actionable insights. IT Professional. Search in Google Scholar

[16] Liang, T., Zeng, B., Liu, J., Ye, L., & Zou, C. (2018). An unsupervised user behavior prediction algorithm based on machine learning and neural network for smart home. IEEE Access, 6. Search in Google Scholar

[17] Zhu, Z., Wang, J., Wang, X., & Wan, X. (2016). Exploring factors-of user’s peer-influence behavior in social media on purchase intention: evidence from qq. Computers in Human Behavior, 63(oct.), 980-987. Search in Google Scholar

[18] Scholz, M., Schnurbus, J., Haupt, H., Dorner, V., Landherr, A., & Probst, F. (2018). Dynamic effects of user- and marketer-generated content on consumer purchase behavior: modeling the hierarchical structure of social media websites. Decision Support Systems, 113(SEP.), 43-55. Search in Google Scholar

[19] Junichiro, N., & Takahiro, H. (2017). Predicting purchases with using the variety of customer behaviors: analysis of the purchase history and the browsing history by deep learning. Transactions of the Japanese Society for Artificial Intelligence, 32(2), B-G63_1-9. Search in Google Scholar

[20] Fan, Z. P., & Sun, M. (2015). Behavior-aware user response modeling in social media: learning from diverse heterogeneous dataauthor-name: chen, zhen-yu. European Journal of Operational Research, 241. Search in Google Scholar

[21] Chen, J. V., Yen, D. C., Kuo, W. R., & Capistrano, E. P. S. (2016). The antecedents of purchase and re-purchase intentions of online auction consumers. Computers in Human Behavior, 54(JAN.), 186-196. Search in Google Scholar

[22] Zhang, J., Liu, Y., Mao, J., Ma, W., Xu, J., & Ma, S., et al. (2023). User behavior simulation for search result re-ranking. ACM transactions on information systems. Search in Google Scholar

[23] Alimamy, S., & Kuhail, M. A. (2023). I will be with you alexa! the impact of intelligent virtual assistant’s authenticity and personalization on user reusage intentions. Computers in human behavior. Search in Google Scholar

[24] Liu, H. (2020). Big data precision marketing and consumer behavior analysis based on fuzzy clustering and pca model. Journal of Intelligent and Fuzzy Systems, 40(2), 1-11. Search in Google Scholar

[25] Lee, C., Lim, S., & Ha, B. (2021). Green supply chain management and its impact on consumer purchase decision as a marketing strategy: applying the theory of planned behavior. Sustainability, 13. Search in Google Scholar

[26] Geiler, L., Séverine Affeldt, & Nadif, M. (2022). An effective strategy for churn prediction and customer profiling. Data Knowl. Eng., 142, 102100. Search in Google Scholar

[27] Zhu Tang,Yang Jiao & Mingmin Yuan. (2024). RFM user value tags and XGBoost algorithm for analyzing electricity customer demand data.Systems and Soft Computing200098-. Search in Google Scholar

[28] Haolong Ye,Shaohong Fang & Qun Zhang. (2024). Research on Classification Method of Highway Concrete Water Reducing Agent Manufacturers Based on K-means++ Clustering.Journal of Physics: Conference Series(1), Search in Google Scholar

[29] Pedro Henrique Esteves Trindade,Gustavo Venâncio da Silva,Flávia Augusta de Oliveira & Stelio Pacca Loureira Luna. (2024). Ranking bovine pain-related behaviors using a logistic regression algorithm. Applied Animal Behaviour Science106163-. Search in Google Scholar

[30] Hui Xie,Yin mei Deng,Jiao yan Li,Kai hong Xie,Tan Tao & Jian fang Zhang. (2024). Predicting the risk of primary Sjögren’s syndrome with key N7-methylguanosine-related genes: A novel XGBoost model.Heliyon(10),e31307-. Search in Google Scholar

[31] Luo Jian,Zhang Yan,Gao Yuanyuan & Zhang Jing. (2024). A novel method based on knowledge adoption model and non-kernel SVM for predicting the helpfulness of online reviews.Journal of the Operational Research Society(6),1205-1222. Search in Google Scholar

[32] Pinto Noemi P.,Amaral Jorge L. M.,Lopes Agnaldo J. & Melo Pedro L. (2023). Diagnosis of Respiratory Changes in Cystic Fibrosis Using a Soft Voting Ensemble with Bayesian Networks and Machine Learning Algorithms.Journal of Medical and Biological Engineering(1),112-123. Search in Google Scholar