Massage program intelligent recommendation method and system based on deep learning

Abstract

The invention relates to the technical field of computers, in particular to a massage program intelligent recommendation method and system based on deep learning, and the method comprises the steps ofcollecting basic information of a user, carrying out the data feature selection of the basic information of the user, and generating user information data; obtaining the user information data and therecommendation program matched with the user information data, numeralizing the user information data and the recommendation program matched with the user information data into sample data, and storing the sample data into a sample database; building a neural network model, input parameters of the neural network model being data characteristics of the user information data, and an output result being a recommendation program; and reading the data in the sample database to repeatedly train the neural network model, thereby improving the accuracy of the neural network model and obtaining the trained neural network model. According to the invention, the result can be recommended to the intelligent massage program most conforming to other conditions currently for the massage chair user, so that the user can obtain personalized massage experience efficiently, automatically and accurately.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to an intelligent recommendation method and system for massage programs based on deep learning. Background technique [0002] In the era of information explosion, various industries are seeking to mine and utilize valuable information from massive data, and use this information as a key parameter to improve industry innovation services. In the massage chair industry, the selection of massage programs by users is no longer limited to programmed choices, and more and more users are pursuing personalized massage services. The demand for personalized massage services not only requires hardware customization and automatic adaptation to be upgraded, but also requires the recommendation of intelligent selection of massage techniques and programs. Intelligent selection and recommendation require the use of recommendation systems. In the fields of e-commerce, online education, and online ...

AI Technical Summary

Problems solved by technology

However, under practical conditions, collaborative filtering methods usually face data coefficient and cold start problems

In addition, the commonly used collaborative filtering method uses a shallow model, which cannot learn the deep features of users and items.

In the massage chair industry, it is usually impossible to directly collect basic user information such as user height, age, gender, weight, and customary massage time, and there is a lack of cloud services to analyze and classify the input data, which makes it impossible to recommend accurate personalities for users. Customized massage program

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