Cross-domain medical care equipment recommendation method and system with privacy protection function

Abstract

The invention provides a cross-domain medical care equipment recommendation method and system with a privacy protection function. The method comprises the following steps: acquiring ID data of a target domain about users and items of medical health care equipment; performing one-hot coding representation on the user ID data and the project ID data of the target domain; expanding the one-hot coding representation of the target domain by using the auxiliary domain user domain dependence feature and the auxiliary domain user domain non-dependence feature to obtain an expanded feature; and using the extended features and the trained factorization machine model to predict the score of the target domain user to the item, and recommending the medical health care equipment according to the score result. The method can ensure that other platforms cannot reversely derive the original score information according to the disclosed user potential factor vector, and can effectively solve the problem of original data privacy leakage of the user in the auxiliary domain data migration process.

Description

technical field [0001] The present disclosure relates to the field of Internet information technology processing, and in particular to a method and system for recommending cross-domain medical care equipment with a privacy protection function. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] At present, due to the rapid development of AI and the field of Internet of Things, a large number of smart Internet of Things devices have emerged in all walks of life. In the field of healthcare, smart IoT devices are mainly embodied in healthcare equipment, and there are various types of healthcare equipment in the market, including: smart bracelets, smart rings, sensor headbands and smart glasses, simple handheld devices, etc. The quantity and variety of these devices vary in price, quality, function, etc. Excessive information makes it difficult...

AI Technical Summary

Problems solved by technology

In addition, in the process of using the Funk-SVD model and ONMTF model to decompose the auxiliary domain scoring matrix, the method retains the item latent factor vector and exposes the user latent factor vector (namely domain-dependent features and domain-independent features), which can make the platform unable to The public latent factor vector of the user deduces the original scoring information, which can effectively solve the problem of privacy leakage of the user's original data during the data migration process of the auxiliary domain;

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