대규모 단체급식 데이터를 활용한 음식 메뉴 군집화와 추천 시스템 성능 비교

Abstract

As the amount of data increases exponentially and an analysis method capable of processing it develops, recommendation systems are being used in various fields to propose products and services that reflect individual preferences. When consumers' preferences can be predicted through the recommendation system, demand forecasts for products and services become more accurate, and resources can be operated more efficiently in inventory management, etc. The recommendation system can be used as a method of identifying preferences for large-scale consumers and reflecting them in demand forecasting. But there are few studies that attempts to cluster by using consumer selection information in situations where selection is limited for recommendation of meal menus or performance of the recommendation system. In this study, meal menus were clustered using the consumer's selection information for meal menus generated in large-scale company cafes, and the performance of recommendation system algorithms was compared. Large-scale company cafes have a limit in that they cannot provide a variety of meal menus to satisfy consumers with various preferences. If consumers' preferences are more accurately reflected, consumers' satisfaction can be improved even with limited meal menus, and from the standpoint of providing meals, the goal of efficient operation of resources can be achieved by minimizing leftovers and operating an appropriate inventory of raw materials. Therefore, in this study, we attempted to cluster meal menus by using the information of consumers' choice and non-selection on meal menus that occurred in large-scale company cafes and presented the characteristics of each cluster through the Naive Bayes classifier. In addition, performance differences between the recommendation system algorithms were compared in the case of reflecting only the selection information for the meal menu selected by the consumer from among 2 to 3 selectable menus, and the two cases reflecting by adding competition information of non-selection. As a result of cluster analysis using group meal data, the meal menu could be divided into 8 clusters, and the characteristics of each cluster could be explained by the consumer's demographic information and the characteristics of the meal menu through the Naive Bayes classification. As for the performance of the meal menu recommendation system, the case of recommending the popular menu showed the best prediction performance, and it was found that the performance of the recommendation system was improved by reflecting not only the information on the item selected by the consumer but also the information not selected. This study suggests that the recommendation performance can be improved by constructing a recommendation system by utilizing clustering information in recommending meal menus and reflecting competition information of non-selection when there is an opportunity for selection. In large-scale group catering, research on clustering based on consumer preferences and increasing the accuracy of recommendations can be used as basic data for demand forecasting for efficient management of resources.