Personalized search returns each user more accurate results by collecting the user\u2019s historical search behaviors to infer her interests and query intents. However, it brings the risk of user privacy leakage, and this may greatly limit the practical application of personalized search. In this paper, we focus on the problem of privacy protection in personalized search, and propose a privacy protection enhanced personalized search framework, denoted with FedPS. Under this framework, we keep each user\u2019s private data on her individual client, and train a shared personalized ranking model with all users\u2019 decentralized data by means of federated learning. We implement two models within the framework: the first one applies a personalization model with a personal module that fits the user\u2019s data distribution to alleviate the challenge of data heterogeneity in federated learning; the second model introduces trustworthy proxies and group servers to solve the problems of limited communication, performance bottleneck and privacy attack for FedPS. Experimental results verify that our proposed framework can enhance privacy protection without losing too much accuracy.<\/p>\n","protected":false},"excerpt":{"rendered":"
Personalized search returns each user more accurate results by collecting the user\u2019s historical search behaviors to infer her interests and query intents. However, it brings the risk of user privacy leakage, and this may greatly limit the practical application of personalized search. In this paper, we focus on the problem of privacy protection in personalized 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