Uniform configuration method for integration of industrial wireless network and time-sensitive network

Abstract

The present invention relates to a multi-party semi-supervised learning method for recommending potential users of financial products, and falls within the field of big data recommendation. To address the challenge faced by financial product providers who possess only positive labeled data and thus cannot perform effective customer recommendation, the method conducts multiple rounds of random sampling on unlabeled data from multiple other parties while preserving data privacy and security. Through this process, multiple balanced binary classification datasets comprising both positive and pseudo-negative samples are constructed. Based on each balanced dataset, a vertical federated learning model is then trained using a base estimator. Based on the collective prediction results generated by the vertical federated learning models, reliable positive samples are identified from the unlabeled data. The overall dataset is iteratively updated to repeat the sampling, model training, and prediction processes, enabling the progressive extraction of batches of reliable positive samples. This method effectively supports batch recommendation in scenarios where the party seeking recommendation services possesses only a small number of positive samples while other parties have large volume of unlabeled data. The method significantly improves the reliability, accuracy, and scalability of financial products recommendation to potential users.

Dependencies are inferred from the claim text.