Application Of Collaborative Recommendation Method Based On Genetic Clustering Algorithm In Meal Ordering System

With the popularization of electronic information service platform and the rapid growth of user data,how to find the information needed by users in the limited time is the research hotspot of the current recommendation system.Collaborative filtering recommendation technology is currently the most widely used recommendation technology,but there are two important problems,that is,scalability and quality issues.At present,most of the researches focuses on the quality of recommendation,and there is less research on the scalability problem.Scalability issues lead to an increase in the recommended response time,and whether users can tolerate a long wait is a fundamental measure of the performance of the recommender system.In this paper,we consider the recommendation quality and scalability problems.In order to solve these problems,this paper puts forward the genetic operation in the traditional collaborative filtering recommendation technology.The process of this method is divided into two steps: firstly,the original data is obtained by genetic clustering,and secondly,on the basis of the initial set,the collaborative filtering recommendation of the fusion genetic algorithm is proposed.By using the Glass,Heart,Soybean,Movie Lens data set to evaluate the effectiveness of our method,the results show that the method can effectively solve the traditional collaborative filtering recommendation system scalability problems and recommend quality problems.Finally,we adopt our method to the ordering system.The system is mainly composed of the user ordering module,the system background management module,the waiter management module,and the chef function management module.Intelligent recommendation function is the core sub module of ordering module,By using method,the ordering system achieve collaborative filtering recommendation based on user evaluation and dishes attribute weight.