Research And Development Of Ordering System With Intelligent Recommendation

With the coming of the era of big data,information overload,and how the user get personalized experience has become a concerned problem.In order to solve this problem,the personalized recommendation system has been applied to all aspects entertainment life,gradually changing the way people live.Such as movie recommendation system of Netflix,Amazon's shopping recommender systems,food industry order recommendation system,etc.With the improvement of living standards,puts forward new requirements to the catering industry,the traditional order treasure,no recommendation feature,cannot satisfy the user's personalized pursuit.The rise in recent years,with the tablet as the carrier of the order system,but most of these order system does not have recommendations or recommended dishes is single,can not give users a good personalized experience.Because our country's diet structure is complex,there are many different kinds of food,so the slow progress in the study of food recommendation algorithm.But dishes recommended for users is inseparable,the fast pace of life,people now go to dinner catering enterprises more opportunities.Most of the time,because the user's hurry don't know what kind of food to his taste,so if there is no good dishes recommended at this moment,will cause the user didn't get high quality personalized service.Now the vast majority of catering enterprises still use paper menu without implementation of electronic and information management.Because paper menu page is limited,there is little food source of allusions or combined with food menu or electronic.In the face of all these problems,this paper developed a fusion food culture and intelligent recommendation in an ordered system.The smart order system will become popular a kind of order model in the future.At present,the researchers also have some dishes recommended area in work,some results were obtained.More classic order recommendation system based on association rules;Order based on collaborative filtering recommendation system;Based on data mining,and cloud computing service dishes recommended system;By using the theory of food fitting for mismatched development "point dishes software".Are studied in this paper,the existing food recommendation algorithm,learning,in the new dishes recommended is proposed on the basis of summing up predecessors' experience-food recommendation algorithm based on correlation algorithm.For classification prediction is incremental updating association rules algorithm is studied to improve and implement.Developed a set of intelligent recommended food culture appreciation for the integration of the intelligent ordering system.This system includes three parts,the server side,ordering terminals,kitchen display terminal.The server-the main function for the background order statistical analysis and processing of information.Food recommendation algorithm,to generate recommendation table;Order recommend table dishes recommended terminal traversal.Food culture of appreciation,order,the order;Kitchen display terminal according to the priority of dishes,intelligent sorting order information and display.In order to prove in this paper,the research and development of the system will have good effect of personalized recommendation,we are under the same data set,two algorithms of comparison test.To recommend two kinds of algorithm of table and table after many experiments to produce recommendations for comparison,the final result shows that the proposed food recommendation algorithm based on correlation recommendation accuracy prediction is incremental updating association rules algorithm is superior to the classification.