Global Perception Federated Recommender System for Click-Through Rate Prediction

Yicheng Di, Jiansong Fan, Rui Zhang, Song Shen, Jiayu Bao, Rongsheng Hu, Yuan Liu

Abstract: As communication networks and smart gadgets evolve, researchers are becoming increasingly interested in recommender systems. Accurate click-through rate (CTR) prediction improves the performance of recommender systems. However, most current CTR prediction methods have problems in obtaining multi-level feature representations from user input, resulting in biased prediction outputs. Furthermore, CTR prediction models are frequently large-scale deep models, which limits their operational efficiency. To overcome these difficulties, this work introduces the Global Perception Federated Recommender System for Click-Through Rate Prediction (GPFed). The Global Perception Module, in particular, emphasizes the value of various field embeddings from a global viewpoint, focusing on the most salient intra-class features to improve multi-level feature representations in user data. Second, the Compact Tuning Module uses inner products to reduce model size and compression layers to minimize model parameters, resulting in increased operating efficiency. Furthermore, Device-Level Privacy Protection protects device privacy throughout the federated learning process. Experiments on three public datasets reveal that GPFed performs better and more efficiently. Compared to the best baseline models, GPFed improves performance by 10.85%, 3.72%, and 4.74% on the Criteo, Avazu, and MovieLens datasets, respectively.

External IDs:dblp:conf/icmcs/DiFZSBHL25