摘要
随着经济的发展,人们对饮食的要求逐渐提高,这促进了各种网上订餐系统的兴起和发展。基于此,设计了推荐模块,以提高系统的运行效率。针对网上订餐的特点,引入粒计算思想,利用粒关联规则挖掘算法探索顾客与菜品间的粒关联规则,减少推荐模块运行时间的消耗。同时,采用为菜品添加标志位的方法,避免相同菜品出现于推荐目录,提高推荐的命中率。
With the development of the economy, people＇s dietary requirements have gradually increased, which has promoted the rise and development of various online ordering systems. Based on this, a recommendation module is designed to improve the efficiency of the system. According to the characteristics of online ordering, the grain calculation method is introduced, and the grain association rules between customers and dishes are explored by using the grain association rule mining algorithm to reduce the running time of the recommendation module. At the same time, the method of adding a flag bit to the dish is adopted to avoid the same dish appearing in the recommended directory and improve the recommended hit rate.
作者
邱京伟
Qiu Jingwei(School of Information and Electrical Engineering,Ningde Normal University,Ningde Fujian 352100,China)
出处
《信息与电脑》
2018年第22期115-117,共3页
Information & Computer
基金
福建省中青年教师教育科研项目(项目编号:JAT160529)
宁德师范学院2015年校级科研项目(项目编号:2015Q12)
关键词
订餐系统
推荐模块
粒关联规则
挖掘算法
ordering system
recommendation module
granular association rules
mining algorithm
