Audience Expansion, a technique for identifying new audiences with similar behaviors to the original target or seed users. The major challenges include a heterogeneous user base, intricate marketing campaigns, constraints imposed by sparsity, and limited seed users, which lead to overfitting. In this context, we propose a novel solution named AudienceLinkNet, specifically designed to address the challenges associated with audience expansion in the context of Rakuten's diverse services and its clients. Our approach formulates the audience expansion problem as a graph problem and explores the combination of a Pre-trained Knowledge Graph Embedding Model and a Graph Convolutional Networks (GCNs). It emphasizes the structural retention properties of GCNs, enabling the model to overcome challenges related to cross-service data usage, sparsity and limited seed data. AudienceLinkNet simplifies the targeting process for small and large marketing campaigns and better utilizes demographics and behavioral attributes for targeting. Extensive experiments on our advertising platform, Rakuten AIris Target Prospecting, demonstrate the effectiveness of our audience expansion model. Additionally, we present the limitations of AudienceLinkNet.

Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the heterogeneity of the user base and its sparsity. In this work, we propose a novel framework that unifies the customers' different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. Extensive experiments on real e-commerce and travel datasets demonstrate the effectiveness of our proposed lookalike model for user targeting task.