Progressive Tasks Guided Multi-Source Network for Customer Lifetime . . . In this paper, we propose a novel P rogressive T asks guided M ulti- S ource N etwork (PTMSN) to tackle the aforementioned problems Specifically, a Cascaded Sub-task Module (CSM) is introduced to alleviate data sparsity by modeling reliance between explicit interactions and implicit monetization
Accepted Papers – WSDM 2025 UIPN: User Intent Profiling Network for Multi Behavior Modeling in CTR Prediction Xu Yang (Tencent)*; Guangyuan Yu (Tencent); Jun He (Tencent)
google lifetime_value - GitHub Accurate predictions of customers’ lifetime value (LTV) given their attributes and past purchase behavior enables a more customer-centric marketing strategy One challenge of LTV modeling is that some customers never come back, and the distribution of LTV can be heavy-tailed
Deep Learning Models for Customer Lifetime Value Prediction in E . . . Client lifetime value is an important KPI for companies since it shows how much money customers are projected to spend while they are a part of a company's network Improved CLV model accuracy and predictive capacity are the goals of this research, which use deep learning techniques—specifically, neural networks
Progressive Tasks Guided Multi-Source Network for Customer Lifetime . . . In this paper, we propose a novel Progressive Tasks guided Multi-Source Network (PTMSN) to tackle the afore-mentioned problems Specifically, a Cascaded Sub-task Module (CSM) is introduced to alleviate data sparsity by modeling reliance between explicit interactions and implicit monetization
Billion-user Customer Lifetime Value Prediction: An Industrial-scale . . . In this paper, we propose a complete set of industrial-level LTV modeling solutions Specifically, we introduce an Order Dependency Monotonic Network (ODMN) that models the ordered dependencies between LTVs of different time spans, which greatly improves model performance