Group Behavior-Guided Multi-Density Graph Clustering for Large-Scale Telemarketing Recommendation
- 1School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China
- 2Mashang Consumer Finance Co., Ltd., Chongqing 401121, China
- 3School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
Abstract
Large-scale telemarketing recommendation is crucial for personalized customer outreach in various industries like banking and telecommunications. However, traditional individual-level recommendation approaches suffer from high computational overhead in large-scale scenarios, making group-level recommendation a promising alternative. Nevertheless, existing group-level recommendation methods rely on distance-based similarity metrics that overlook long-tail density variations and decouple clustering from resource allocation, leading to suboptimal allocation decisions. To address these challenges, we propose a novel Group Behavior-guided Multi-density Graph Clustering (GBMGC) framework, which integrates multi-density behavior-aware graph clustering with constrained resource allocation optimization for efficient and scalable telemarketing recommendation. The framework adopts a two-stage design consisting of Density-aware Graph Clustering (DGC) and Group-guided Optimization Recommendation (GOR), with Tree-structured Parzen Estimator (TPE)-based hyperparameter selection. Specifically, to overcome the limitation of conventional distance-based similarity grouping in capturing long-tail behavioral density patterns, DGC constructs a weighted behavioral graph using heuristic weights derived from statistical tests on historical response distributions and applies adaptive density thresholds to identify groups with different population densities. In the second stage, based on the resulting group structure, GOR formulates the scheduling process as a constrained integer programming problem to maximize expected answer rates under practical constraints. Finally, TPE selects the key hyperparameters of DGC and GOR using validation-set performance as feedback, reducing manual hyperparameter search. Evaluated on a financial telemarketing test dataset with more than 3 M records and the MovieLens-1 M test dataset with approximately 0.2 M records, GBMGC achieves higher answer rate (AR) point estimates than the compared methods on the historically matched observations under the adopted offline evaluation protocol, while maintaining favorable computational efficiency. The results on MovieLens-1 M provide additional evidence of its applicability beyond the telemarketing recommendation setting.
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BibTeX
@article{wang2026group,
title = {{Group Behavior-Guided Multi-Density Graph Clustering for Large-Scale Telemarketing Recommendation}},
author = {Ke Wang and Wanjie Tao and Yuchen Yuan and Zhang-Hua Fu and Xin Cai and Yanyan Zheng and Quan Lu and Ning Jiang and Yuanyuan Liu},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14203797},
url = {https://www.mdpi.com/2227-7390/14/20/3797},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Group Behavior-Guided Multi-Density Graph Clustering for Large-Scale Telemarketing Recommendation
AU - Ke Wang
AU - Wanjie Tao
AU - Yuchen Yuan
AU - Zhang-Hua Fu
AU - Xin Cai
AU - Yanyan Zheng
AU - Quan Lu
AU - Ning Jiang
AU - Yuanyuan Liu
PY - 2026
DA - 2026/10/19
JO - Mathematics
DO - 10.3390/math14203797
UR - https://www.mdpi.com/2227-7390/14/20/3797
AB - Large-scale telemarketing recommendation is crucial for personalized customer outreach in various industries like banking and telecommunications. However, traditional individual-level recommendation approaches suffer from high computational overhead in large-scale scenarios, making group-level recommendation a promising alternative. Nevertheless, existing group-level recommendation methods rely on distance-based similarity metrics that overlook long-tail density variations and decouple clustering from resource allocation, leading to suboptimal allocation decisions. To address these challenges, we propose a novel Group Behavior-guided Multi-density Graph Clustering (GBMGC) framework, which integrates multi-density behavior-aware graph clustering with constrained resource allocation optimization for efficient and scalable telemarketing recommendation. The framework adopts a two-stage design consisting of Density-aware Graph Clustering (DGC) and Group-guided Optimization Recommendation (GOR), with Tree-structured Parzen Estimator (TPE)-based hyperparameter selection. Specifically, to overcome the limitation of conventional distance-based similarity grouping in capturing long-tail behavioral density patterns, DGC constructs a weighted behavioral graph using heuristic weights derived from statistical tests on historical response distributions and applies adaptive density thresholds to identify groups with different population densities. In the second stage, based on the resulting group structure, GOR formulates the scheduling process as a constrained integer programming problem to maximize expected answer rates under practical constraints. Finally, TPE selects the key hyperparameters of DGC and GOR using validation-set performance as feedback, reducing manual hyperparameter search. Evaluated on a financial telemarketing test dataset with more than 3 M records and the MovieLens-1 M test dataset with approximately 0.2 M records, GBMGC achieves higher answer rate (AR) point estimates than the compared methods on the historically matched observations under the adopted offline evaluation protocol, while maintaining favorable computational efficiency. The results on MovieLens-1 M provide additional evidence of its applicability beyond the telemarketing recommendation setting.
SN - 2227-7390
ER -