The Design And Implementation Of Food Ordering Recommendation System Based On Collaborative Filtering

With the arrival of the era of big data and artificial intelligence,the traditional industries have undergone enormous changes.The catering industry is no exception,but it will also face many challenges in the process of transformation.Applying the classic method in different fields will also face many challenges.For this reason,in this thesis,when the recommendation system is applied to the field of gourmet food,the characteristics of the fusion food field improve the traditional user-based collaborative filtering algorithm.The improved algorithm is applied to the actual meal to solve the actual problems faced,and a WeChat food recommendation system based on the B/S structure is designed and implemented.The work done in this thesis is summarized as follows:When the user-based collaborative filtering algorithm is applied to the ordering field,the first problem to be solved is the problem that the user's dietary preference changes with the season.This thesis mainly adds the seasonal weights of the impacting factors when predicting the user's interest degree,in order to balance the influence of seasonal factors on dishes,and to improve the effectiveness of dish recommendations.Secondly,it deals with the collocation of dishes.This article builds a collocation rule based on association rules,and then generates dishes collocation library.When any of the dishes in the collaborative filtering recommendation menu is selected by the user,the system automatically matches the dishes collocation library and finds the corresponding dishes to be recommended for collocation,so that a scientific and healthy and reasonable meal can be achieved.Finally,an experimental analysis was performed on 5618 rating datasets of a restaurant in Hubei Province.The traditional user-based collaborative filtering algorithm was compared with the improved algorithm based on the system performance and the recommended quality of the dishes.The differences between them are discussed.In the actual effect of the recommendation,the seasonal weights of the influence factor dishes and the influence of the food mix on the recommendation algorithm are added.The experimental results show that the improved algorithm can effectively solve the seasonal problems of dishes,and the addition of dishes can make the user's meal more scientific and healthy.Combining with actual needs,this paper applies the improved algorithm to the actual ordering,and builds a food recommendation system based on B/S framework.The design and implementation of the food ordering recommendation system not only helps users quickly find the dishes they like to eat,but also the restaurant can find more users and increase the restaurant's turnover,achieving a win-win result.