摘要
为了更有效地向用户推荐其感兴趣的菜品,提升餐营业的精准服务水平,本文基于Alternating Least Squares交替最小二乘法和Apache Spark计算引擎,开发了一款根据用户周期性消费行为的菜品智能推荐系统。系统将采集历史消费数据存于HBase并训练推荐模型,通过Thymleaf展示推荐信息。系统采用冷热两种启动模式来满足用户需求。
In order to more effectively recommend the dishes of interest to users and improve the precise service level of the restaurant business,this paper develops a dish based on the user's periodic consumption behavior based on the ALS(Alternating Least Squares)alternating least squares method and the Apache Spark computing engine.Intelligent recommendation system.Store the collected historical consumption data in HBase,train the recommendation model,and display the recommendation information through Thymleaf.The system adopts two startup modes of hot and cold to meet user needs.
作者
王程
唐建国
WANG Cheng;TANG Jianguo(School of Informatics,Henan University of Technology,Zhengzhou,China,450001)
出处
《福建电脑》
2023年第3期78-81,共4页
Journal of Fujian Computer
关键词
交替最小二乘法
计算引擎
菜品
推荐系统
Alternating Least Squares Method
Computing Engine
Dishes
Recommendation system
