基于Spark的菜品推荐系统的设计与实现

Design and Implementation of Vegetable Recommendation System Based on Spark

随着互联网的快速发展,各行各业都在不断引入新的信息技术,在大数据环境下如何通过信息化技术推测顾客对菜品、口味的偏好,并向顾客智能推荐菜品结果成为了新的需求。为便于顾客在众多的菜系中选择符合自身口味的菜品,设计开发了一款个性化菜品推荐系统,系统采用协同过滤推荐算法,根据顾客对菜品的历史评分数据,使用Hadoop平台对其进行存储,然后通过Spark框架MLlib库中的ALS算法进行计算,得到菜品推荐列表,最终通过页面向用户进行展示,在一定程度上满足了顾客的个性化需求。

With the rapid development of the Internet,all walks of life are constantly introducing new information technology.In the big data environment,how to use information technology to infer customers’preferences for dishes and tastes,and intelligently recommend dishes to customers has become a new demand.In order to make it easier for customers to choose dishes that suit their own tastes,a personalized dish recommendation system is designed and developed in this paper.Then,the ALS algorithm in the MLlib library of the Spark framework is used for calculation to obtain a recommended list of dishes,which is finally displayed to the user through the page,which satisfies the personalized needs of customers to a certain extent.