基于微信小程序的校园外卖订餐个性化推荐系统

摘  要

传统餐饮行业信息化程度较低，点餐的过程基本都是人工进行的。这种方式在人多的时候效率就比较低下，对于消费者来说也不太方便。国外因为软硬件设施技术的领先比我们移动互联网起步要早，在点餐智能化方面有一些值得借鉴的案例。随着国内的基础设施日趋完善，4G的普及，5G网络的即将兴起以及智能手机的普及，移动信息时代的大潮已经来临。IT行业中最热点开始聚焦到了移动互联网上。移动互联网的广泛流行促使我思考这样一个问题：能不能结合当下的技术和工具创造出一种更好的手段用更低廉的成本来让更多的餐馆可以更好地被管理。应此本课题应运而生。

课题设计校园外卖个性化推荐系统是一款英语学习软件，该系统是基于C/S+B/S模式来设计，技术上采用小程序+JSP网页技术开发，利用Java技术来实现，通过MySQL数据库来存取校园外卖个性化推荐系统相关的信息，小程序注册用户和商家用户可餐厅列表、购物车订单及个人中心等，后台管理端可对菜品信息、菜品类别信息、优惠资讯管理、订单管理、统计及用户管理等。

关键词：个性化推荐；订餐；JSP开发技术；MySQL数据库；小程序技术

PERSONALIZED RECOMMENDATION SYSTEM FOR CAMPUS TAKEOUT BASED ON WECHAT PLATFORM

ABSTRACT

The traditional catering industry has a low degree of informatization, and the process of ordering is basically manual. This way is inefficient when there are many people, and it is not very convenient for consumers. Because of the leading technology of software and hardware facilities, foreign countries started earlier than our mobile Internet, and there are some cases worthy of reference in the aspect of intelligent ordering. With the improvement of domestic infrastructure, the popularity of 4G, the rise of 5g network and the popularity of smart phones, the tide of mobile information age has come. The hottest spot in IT industry began to focus on the mobile Internet. The popularity of mobile Internet makes me think about this question: can we combine the current technology and tools to create a better means to make more restaurants better managed with lower cost. In response to this, this topic came into being.

The system is designed based on C/S + B/S mode. It is technically developed by using small program + JSP Web technology and realized by using Java technology. It can access the relevant information of campus takeout personalized recommendation system through MySQL database. Registered users and business users of small program can access restaurant list, shopping cart order and other information Personal center and so on. The background management terminal can manage the dish information, dish category information, discount information, order management, statistics and user management.

KEY WORDS: Personalized recommendation; Ordering; JSP development technology; MySQL database; small program technology