Transferable and Forecastable User Targeting Foundation Model

TL;DR

This paper introduces FOUND, a foundation model for user targeting that enhances transferability across domains and improves forecastability by integrating multi-scenario data and future behavior descriptions, demonstrating superior real-world performance.

Contribution

The paper presents FOUND, a novel user targeting foundation model that addresses transferability and forecastability challenges in digital marketing through contrastive pre-training and future behavior modeling.

Findings

Outperforms existing baselines in cross-domain scenarios

Successfully deployed on Alipay platform

Widely used across various industrial scenarios

Abstract

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.