Design And Implementation Of Fitness Recommendation System Based On Knowledge Graph

As the material life of the Chinese people has become more abundant,their spiritual pursuits have also increased,and fitness needs have become stronger.National fitness studios have sprung up,but they are faced with unprofessional fitness coaches,expensive facades,high costs,and risk resistance.The traditional fitness industry is gradually weakening due to difficulties such as weak abilities.The online fitness industry is gradually emerging,but in the era of information explosion,how to select the most suitable fitness information from the massive fitness knowledge is also a big problem that needs to be solved urgently.The recommendation system can solve the information overload,change the information filtering process from "user active search" to "system active push",and solve the current dilemma of users.This article creatively tries to combine knowledge graphs and collaborative filtering algorithms,builds a system through B/S architecture,uses My SQL database,applies Vue.js front-end,and uses Flask back-end framework technology to design and implement a fitness recommendation system based on knowledge graphs..This article first researches and analyzes the development trend of the fitness industry and recommendation system,and determines the research content and direction;then introduces the development of the system front-end and back-end tools and recommended basic algorithms;then conducts a detailed analysis of functions,safety,and performance requirements,and divides the system For the user module and the back-end management module;then the algorithm for obtaining user portraits and content portraits is designed in detail,and the algorithm design process and calculation results are displayed;the workflow of the personalized recommendation module is designed,and finally the system functions are tested and the effect diagram is displayed.The core work of this system is to process the fitness article data set,obtain content portraits and user portraits,and input them into the recommendation algorithm.After the knowledge graph is optimized,the final recommendation results are displayed to users.This system builds a knowledge graph and a recommendation system.After testing and actual use,this system can support users' self-health evaluation,meets the expected functional and safety requirements,and can provide users with high-accuracy recommendation results to improve recommendations.The accuracy rate provides a new idea.