The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Data-driven personalized marketing strategy optimization based on user behavior modeling and predictive analytics: Sustainable market segmentation and targeting
Open accessAbstract
Personalized recommendation remains a central challenge in modern marketing systems due to the complexity of user-product-query interactions. In this study, we propose a novel framework called DP-GCN (Deterministic Policy Graph Convolutional Network), which integrates multi-level Graph Convolutional Networks (GCNs) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning to model heterogeneous information networks composed of users, products, and search queries. The proposed framework consists of three key components: (1) a graph-based embedding module to capture multi-relational structures; (2) a fusion layer that integrates dynamic and static features from users and items; and (3) a reinforcement learning layer that adaptively updates recommendation policies based on user feedback. We evaluate our model on several public benchmark datasets and a real-world dataset collected from a local e-commerce platform. Results demonstrate that DP-GCN consistently outperforms state-of-the-art baselines in AUC, Precision@K, and NDCG@K. The findings highlight the effectiveness of combining graph-based relational modeling with reinforcement learning for improving both the accuracy and adaptability of personalized recommendation systems.
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“The 1-10-100 rule illustrates that prevention ($1) is far cheaper than correction ($10) or failure costs ($100) [33,73].”
From Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles (2025) · cites this paper as [73]
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Cites
Deep Interest Network for Click-Through Rate Prediction
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Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
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Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles
Guillen-Aguinaga, M., Aguinaga-Ontoso, E., Guillen-Aguinaga, L., Guillen-Grima, F., & Aguinaga-Ontoso, I. (2025) · Data · doi:10.3390/data10120201 · Cited by 37
Li, L., & Jiang, Y. (2026) · AIMS Mathematics · doi:10.3934/math.2026130 · Cited by 4
Alsayat, A., Alshardan, A., Baihan, M., Alshakhs, A., Althobaiti, T., Maray, M., Hasanin, T., & Duhayyim, M. A. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114806
Yadav, M., Banerji, P., & Malik, N. (2026) · SN Business & Economics · doi:10.1007/s43546-026-01178-6
Komara, S., Čvirik, M., & Kuchta, M. (2026) · International Scientific Conference Central and Eastern Europe in the Changing Business Environment · doi:10.18267/pr.2026.vol.2587.16
Metadata
- Authors
- Sun, B.
- Affiliations
- Changchun Finance College (CN)
- Corresponding author
- Sun, Bin
- ORCID
- Sun, Bin
- Venue
- PLOS One
- Publisher
- Public Library of Science (PLoS)
- Published
- 2025-07-24
- Volume
- 20
- Issue
- 7
- Pages
- e0328151
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC12288996
- OpenAlex
- W4412629852
- Cited by
- 6
- References
- 31
- Citations by year
- 2026: 5 · 2025: 1
- FWCI
- 6.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- People's Government of Jilin Province
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- personalized recommendation, personalized marketing, user behavior modeling, market segmentation, market targeting, graph convolutional networks, deep deterministic policy gradient, reinforcement learning, heterogeneous graphs, e-commerce recommendation, feature fusion
- MeSH terms
- Algorithms, Humans, Neural Networks, Computer, Marketing
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