Transferable and Forecastable User Targeting Foundation Model
This addresses challenges for non-expert marketers in digital marketing by improving user targeting across diverse industrial scenarios, though it appears incremental as it builds on existing foundation model concepts.
The paper tackles the problem of poor cross-domain transferability and insufficient forecastability in user targeting for digital marketing by proposing FOUND, a foundation model that integrates heterogeneous multi-scenario data and uses contrastive pre-training, achieving significant outperformance over baselines in real-world scenarios and deployment on the Alipay platform.
User targeting, the process of selecting targeted users from a pool of candidates for non-expert marketers, has garnered substantial attention with the advancements in digital marketing. However, existing user targeting methods encounter two significant challenges: (i) Poor cross-domain and cross-scenario transferability and generalization, and (ii) Insufficient forecastability in real-world applications. These limitations hinder their applicability across diverse industrial scenarios. In this work, we propose FOUND, an industrial-grade, transferable, and forecastable user targeting foundation model. To enhance cross-domain transferability, our framework integrates heterogeneous multi-scenario user data, aligning them with one-sentence targeting demand inputs through contrastive pre-training. For improved forecastability, the text description of each user is derived based on anticipated future behaviors, while user representations are constructed from historical information. Experimental results demonstrate that our approach significantly outperforms existing baselines in cross-domain, real-world user targeting scenarios, showcasing the superior capabilities of FOUND. Moreover, our method has been successfully deployed on the Alipay platform and is widely utilized across various scenarios.