Abstract
Recent embedding techniques have shown remarkable effectiveness in CTR prediction. However, such methods heavily rely on centralized storage and work poorly on cold-start users and items. In reality, historical data of users or ads are heterogeneously distributed across multiple platforms and not directly accessible due to business competition and privacy issues, leading to cold start problems with CTR predictions for each application server. In this paper, we learn federated meta embedding (FME) based on the cooperation of the user-server-advertiser (H-USA), which completes the CTR prediction task in two stages. In the federated phase, we learn richer semantic information about hot IDs under the collaboration of the USA. We treat each cold id in the meta phase as a learning task. And then, the application server learns embedding for cold IDs using federated embeddings through gradient-based meta-learning. Extensive experiments on real-world datasets show that FME learned in H-USA can significantly improve the prediction performance of cold IDs.
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Rong, X., Zhu, J., Xi, H. (2022). A Collaborative Framework for Ad Click-Through Rate Prediction in Mobile App Services. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fernández, P., Ruiz-Cortés, A. (eds) Service-Oriented Computing. ICSOC 2022. Lecture Notes in Computer Science, vol 13740. Springer, Cham. https://doi.org/10.1007/978-3-031-20984-0_40
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