Design And Implementation Of Intelligent Recommendation Ordering System Based On WeChat Small Program

In recent years,with the rapid development of the Internet industry,many traditional industries begin to combine with the Internet and create a new development ecology through digital transformation and upgrading.Especially in the aspect of "eating" that Chinese people pay the most attention to,the catering industry transfers a series of service processes such as ordering and settlement from offline to online,so that customers can enjoy more convenient,fast and free services by using mobile devices.But many of today's ordering systems just digitize the original text information,instead of giving customers personalized service.Other applications in the e-commerce,such as Taobao APP and Jingdong APP,have been able to provide users with personalized recommendation services through the recommendation system which can automatically find out the commodities they may be interested in,while the current ordering systems do not have such functions.For this reason,an ordering system in which a recommendation system is added to provide personalized recommendation services for users ordering food in the restaurant has been designed.This recommendation system uses a hybrid recommendation algorithm based on collaborative filtering recommendation algorithm.Traditional collaborative filtering-based recommendation algorithms have problems such as high data sparseness,poor recommendation interpretation,and cold start.According to these problems,the characteristics of recommendation algorithms based on content and association rules can be used to transform the recommendation algorithm based on collaborative filtering.The core idea is to predict the score of items by the algorithm based on association rules and use the predicted score to fill the data set to reduce the sparsity of the data,then predict the user score by combining the similarity of items calculated based on content and collaborative filtering,and generate recommended items for target users based on the score.When recommending dishes for users,we should not only rely on the results generated by the recommendation algorithm,but also need to combine certain recommendation strategies to sort and select the combination of items and finally recommend a suitable number of dishes for the user.Therefore,a recommendation strategy is designed to recommend to the user a set of dishes with a proper number and a reasonable combination of meat and vegetables according to the number of diners and the classification information of the dishes.According to the needs of consumers and businesses in the catering industry,an intelligent recommended ordering system including a We Chat mini-program for ordering food and catering management applications has been designed and implemented.The system can not only help consumers quickly find their favorite dishes and enjoy free and convenient ordering services,but also help merchants manage catering related information and understand their own operating conditions.