Federated learning-based recommendation model training method, terminal and storage medium

Abstract

The invention discloses a federated learning-based recommendation model training method, a terminal and a storage medium. The method comprises the steps of obtaining a user historical behavior data set recorded by a client application in multiple preset types of application projects; extracting a single characteristic user vector of each client application based on each group of user historical behavior data set; extracting a project feature vector set and a project score set from a user historical behavior data set of the target application; combining each single feature user vector, the project feature vector set and the project score set to obtain a local training sample set; and participating in federated learning based on the local training sample set to obtain a recommendation modelof the target type application project. According to the method, model training is carried out under a federal framework to protect user privacy data, meanwhile, recommendation model training is carried out on the basis of multi-scene data, the recommendation model obtained through training can more accurately locate the preference characteristics of the user, and therefore the recommendation effect of the recommendation model is improved.

Description

technical field

[0001] The invention relates to the technical field of artificial intelligence, in particular to a federated learning-based recommendation model training method, a terminal and a storage medium. Background technique

[0002] With the development of artificial intelligence and machine learning technology, the recommendation system that intelligently recommends music, movies, shopping products and other items for users is also constantly developing. The current recommendation system will upload the user's historical data information to the central database, and gather the data together for the training of the recommendation model. However, uploading user data to the central database will have the risk of leaking user privacy. With the introduction of the concept of federated learning, model training can be carried out under the federated learning architecture, so that users' private data can be combined with multiple clients for effective model training without...

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