Design And Implementation Of Recommended Order System Based On Random Forest Model

With the widespread application of ordering systems in the catering industry,ordering systems with recommendation functions are increasingly receiving attention.However,traditional ordering systems have some problems,such as difficulty in consumer selection and inaccurate recommendations.Therefore,ordering systems with recommendation functions have important research background and practical significance.Based on the characteristics of traditional ordering systems and the random forest model,this thesis designs and implements an ordering system with recommendation functions.The main work includes the following aspects:(1)Designing and implementing a We Chat endpoint meal mini-program that provides consumers with functions such as scanning codes,logging in,browsing menus,placing orders,queuing,and recommending.The We Chat mini-program serves as the front-end interface,allowing consumers to easily order food through their phones,saving time and energy.Additionally,emphasis is placed on the user experience of the mini-program,enhancing consumers’ user experience through concise and clear interface design and smooth operation.(2)Designing and implementing a restaurant management terminal that provides functions such as dish management,order processing,and data statistics.This facilitates business management and monitoring by restaurant management personnel.Serving as the backend system,the restaurant management end offers powerful functions and flexibility,enabling restaurant operators to monitor order status in realtime,manage dish inventory,and collect sales data.The restaurant management end improves the efficiency and management level of the restaurant,providing better operational support.(3)Comparing the performance of decision trees,SVM,content-based recommendation,user-based collaborative filtering,item-based collaborative filtering,and random forest,it is determined that random forest is the preferred recommendation strategy.A dish intelligent recommendation strategy is built based on the random forest model.The system recommends dishes to users,and then applies the Latent Dirichlet Allocation(LDA)model to extract the consumer’s preferences in terms of keywords and their weights.These preferences are then incorporated into the system to enable personalized dish recommendations,aiming to enhance consumer satisfaction and dining experience.(4)Deploying the system to the server to ensure stable operation and meet consumer needs.This involves achieving remote access and data storage of the system,ensuring system availability and security.(5)Conducting comprehensive performance verification and functional testing of the system to evaluate its performance in terms of concurrent access,response time,and stability.Additionally,functional testing is conducted to ensure that the system operates as expected and to identify any potential issues and errors.The test results indicate that the system can meet performance and functional requirements,and can provide consumers with a good ordering experience.The originality of this paper lies in the systematic recommendation strategy,which applies consumer scores to the random forest model to obtain recommended dishes and combines it with the Latent Dirichlet Allocation(LDA)model to extract keywords and weights representing consumer preferences.By integrating comprehensive rating information,incorporating the LDA model,and leveraging personalized recommendation capabilities,it aims to accurately understand consumers’ preferences,provide personalized recommendation results,and offer consumers a smart recommendation experience that caters to their needs.The system designed in this article achieves intelligent recommendations by comprehensively utilizing consumer ratings and model analysis.The intelligence is manifested in accurately understanding consumer preferences and transforming them into actionable keywords and weights for providing personalized recommendation results.Through comprehensive analysis and an intelligent recommendation process,the system gains a deeper understanding of consumers’ preferences and provides them with an intelligent,personalized dish recommendation experience.