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Cited by 11Recommender Systems and TechniquesInformation Retrieval and Search BehaviorCustomer churn and segmentation

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 access
Sun, Bin·2025·PLOS One
DOI 10.1371/journal.pone.0328151PMC PMC12288996
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Abstract

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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Crossref · 10.1371/journal.pone.0328151

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Fig 1 The framework for GCN.
Fig 1 The framework for GCN.CC BY · PubMed Central
Fig 2 The framework for DP-GCN. This study adopts a three-object heterogeneous information network comprising users, items, and queries as input to model and optimize the complex relationships involved in personalized recommendations. First, I apply tokenization to the input data, breaking down the textual information of users, items, and queries into word units. We then generate initial embeddings for users, items, and queries using pre-trained vectors or random initialization with the same dimensionality. These embeddings capture the basic semantic information of each entity, laying the foundation for subsequent relationship modeling.
Fig 2 The framework for DP-GCN. This study adopts a three-object heterogeneous information network comprising users, items, and queries as input to model and optimize the complex relationships involved in personalized recommendations. First, I apply tokenization to the input data, breaking down the textual information of users, items, and queries into word units. We then generate initial embeddings for users, items, and queries using pre-trained vectors or random initialization with the same dimensionality. These embeddings capture the basic semantic information of each entity, laying the foundation for subsequent relationship modeling.CC BY · PubMed Central
Fig 3 Training process and AUC value on the Electronics dataset.
Fig 3 Training process and AUC value on the Electronics dataset.CC BY · PubMed Central
Fig 4 The comparison result on the Electronics dataset.
Fig 4 The comparison result on the Electronics dataset.CC BY · PubMed Central
Fig 5 The comparison result on the Books dataset.
Fig 5 The comparison result on the Books dataset.CC BY · PubMed Central
Fig 6 The prediction time comparison on Books dataset.
Fig 6 The prediction time comparison on Books dataset.CC BY · PubMed Central
Fig 7 The comparison result on the self-established dataset.
Fig 7 The comparison result on the self-established dataset.CC BY · PubMed Central
Fig 8 The user satisfaction comparison.
Fig 8 The user satisfaction comparison.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Y Zhang, 2022, PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems[C]//2022 IEEE 38th International Conference on Data Engineering (ICDE, 3453

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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)
DOI
10.1371/journal.pone.0328151
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
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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