Group Behavior-Guided Multi-Density Graph Clustering for Large-Scale Telemarketing Recommendation

Source 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.