Android点菜软件-外文及翻译

译文题目原稿题目
毕业设计（论文）
原稿及译文
基于安卓系统的电子菜单软件Intelligent e-Restaurant using Android
OS
原稿出处International Conference & workshop on Advanced Computing 2013(ICWAC2013)
Intelligent e-Restaurant using Android OS ABSTRACT：The simplicity and ease of access of a menu are the main things that facilitate ordering food in a restaurant. A Tablet menu completely revolutionizes the patron’s dining experience. Existing programs provide an app that restaurants can use to feed their menus into IOS & Android based tablets and make it easier for the diners to flip, swipe & tap through the menu. We here aim to provide the restaurants with a tablet menu that would recommend dishes based on are commendation algorithm which has not been implemented elsewhere. In addition to this we run the app on an Android based tablet & not on an IOS based tablet which is more expensive alternative. We use a cloud-based server for storing the database which makes it inexpensive&secure.
Keywords：Recommendation, Tablet, Application, Restaurant Menu, Intelligent, Android
1. INTRODUCTION
Over the years, technology has tremendously revolutionized the restaurant industry. But much of the innovation has been with point-of-sale (POS) operations. Yet other areas of a restaurant are ripe for innovation, such as the menu. Traditional restaurant service requires waiters to interact with customers directly before processing their orders. However, a high-quality recommendation service system would actively identify customers and their favorite meals and expenditure records.
There is a famous saying that “People eat with their eyes”. The e-Menu provides additional information about menu items and drinks than a traditional paper menu. With interactive pictures it gives additional information about the food item. Tablets are said to eliminate order-taking errors from the waiters. In the kitchen, there is less confusion as everything is now written clearly. Developers of similar applications maintain that customers who seat at tables outfitted with tablets spend about 20% more than those at other tables. With the visuals, you know exactly what you’re going to get in your plate. The service goes quicker. Tablets are said to allow cutting the
labor expenses.Customers feel more involved in the process. Restaurants can build their e-reputation and customer community in live.
The restaurant menu has evolved from its humble beginnings on carte chalkboards and imageless print to today’s detailed, colorful displays. With the emergence of digital tablets and user-friendly touch screen technology menus can move to a whole new surface. With this electronic menu, orders can be taken correctly the first time. There is no need to run back and forth to a distant terminal, because the terminal is always with the server. Every order is associated with an individual seat at the table, and orders are built one customer at a time, just like on paper, but with greater accuracy.Items can also easily be shared by the whole table, moved or modified,and noted and the cost can be calculated in real time.
The Recommendation algorithm suggests dishes to the patrons based on previous orders. It makes it easier for the customer to build his/her order and also view the most popular dishes. Moreover,various dimension filters can be used according to individual preferences e.g.Price,taste, quantity,etc.
There are several restaurants in Mumbai which have replaced the traditional paper menus with the digitized tablet menu. But none of the apps let the patron place an order directly to the kitchen. The tablet’s use is restricted to simple viewing of the menu.
2. EXISTING SYSTEMS
2.1 Conventional Systems
Restaurant services such as making reservations, processing orders, and delivering meals generally require waiters to input customer information and then transmit the orders to kitchen for meal preparation. When the customer pays the bill, the amount due is calculated by the cashier.
Although this procedure is simple, it may significantly increase the workload of waiters and even cause errors in meal ordering or in prioritizing customers, especially
when the number of customers suddenly increases during busy hours, which can seriously degrade the overall service quality.
2.2 Electronic POS Terminals
A very commonly implemented system,currently being used by numerous restaurants and chains all over the world, is the electronic point-of-sale terminal system.
Here, the servers/waiters generally take the order from the customer and head onto a terminal, where they can feed the order into a computer. The order can then be transmitted to the kitchen automatically via the terminal through a network, or it may even be delivered manually by the server to the kitchen.
Although a huge improvement over the pen and paper still prevalent over the world, this does not have much value addition for the customer and mostly only benefits the establishment and the administration of the establishment.
2.3 Tablet Based Menu
With the current onslaught and popularity of touch based devices, especially the Apple iPad, it did not take long for the tablet based menus to make an entrance in to the market.
In their current state, these menus are just a glorified version of their paper based counterparts.
The workflow, even with these tablet menus, remains the same as the one mentioned in section 2.1 or a combination of the previous two. There is very little value addition to the customer and the establishment or its staff.
The available technology is definitely not being utilized to its maximum potential.
3. PROPOSED SYSTEM
3.1 System Architecture
The following figure (Fig3.1), demonstrates the basic architecture of our system. Understanding the other intricacies and details of the system will become a lot easier if one goes through this figure,before diving into the rest of it.
Fig 3.1Basic System Architecture and System Components
The system will consist of the following components:The backend,which is made up of the web server and the database, and the frontends that include both the patron frontend (delivered as a native mobile application) and the administration or the kitchen frontend(delivered as a web application).
This system is based on the very popular Model-View-Controller(MVC) architecture. MVC is most commonly used in websites, very popular and tried and tested.
None of the frontends directly “talk”to the database. They instead rely on RESTFUL web services that can be used to perform CRUD operations on the database.
Fig3.2Flowchart
The following figure (Fig3.2)demonstrates the flow of our tablet application. 3.2 System Overview
The most important components of this system are the database and the patron frontends or the tablet applications.
Providing value to both the business and its patron is an important objective, but we believe that one follows the other. Following that belief, the customer is given a whole lot of importance.
The patron will be presented with a tablet, running the Android OS. This tablet will be synchronized with the database running on our centralized cloud powered servers.
The menu data, upon synchronization, is stored on locally on the tablets so that the user, i.e. patron, need not wait for the menu to be downloaded from the servers. This will allow faster access to the menu.
The user can then browse the menu however they want to, sorting the items on various dimensions like price, popularity, ratings, etc. The user can also click through to view more information about any item like nutritional information, ingredients, trivia and any other content that the restaurant administration may feel like including.
The user can also view personalized recommendations for items that they may like. This is one of the most important aspects of our system that not only enhances the customer experience but can also help increase revenue for the business.
While browsing through the menu, the customer may add items to his/her order. This process is commonly known as “building the order”. After the order is built and read, the user may go ahead and place the order. The staff will automatically and almost instantly be notified about the new order so that they can act on it.
If the establishment allows, the user may even track the status of their order so that they know when to expect their food and drinks to land up on their table.
3.3 Recommendation Overview
The recommendation algorithm is an innovative feature that we aim to include in our menu. When most tablet menus provide the customers with only a simple menu, this system will provide recommendations which will make it easier to build an order considering what other customers have ordered previously or the similarities between various dishes.
Recommendation systems using sets were considered.
We finally decided to use the below methodology, which has been discussed in an earlier study.
The algorithm mainly has5parts:
ers- a certain number of people are made to rate individual food items.
2.Entities- the food items.
3.Value Dimensions - the categories that are formed to rate the food items e.g. Price,quality,meat content, etc.
4.Belief System - is personal to each user & allows telling the system what ideal value they want each value dimension to have.
5.Ideal candidate - set of ideal value dimensions that are formed on the basis of
a weighted average.
Each User rates a food item on a scale of1to 5 with respect to two things:
er’s ideal value dimension.
2.The weight or the importance of that value dimension.
With the food items set in place, our next task was to analyze the various
attributes that were associated with each food item. We applied a food item click counter to the entire data set, which produced a list of the most viewed food item. After listing out the Top N clicked food-items by the customers,we apply normalization to the retrieved list of food-items,to filter out redundant clicks.
We further investigate the levels of commonality that existed between various pairs of food items. Jaccard’s co-efficient was used to calculate the degree of similarity.
The Jaccard index, also known as the Jaccard similarity coefficient (originally coined coefficient de communaute by Paul Jaccard), is a statistic used for comparing the similarity and diversity of sample sets.
The Jaccard coefficient measures similarity between sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets:
Collaborative filtering-based recommender systems rely on information derived from social activities of the users, such as opinions or ratings, to form predictions or produce recommendation lists.Existing collaborative filtering techniques involve generating a user-item matrix, from which recommendation results could be derived Content-basedfiltering focuses on the selection of relevant items from a Content-based filtering focuses on the selection of relevant items from a large data set, things that a particular user has a high probability of liking. This involves training the data set with machine learning techniques. Clustering involves sectioning the data set into particular sets, each of which corresponds to certain preference criteria. Also, typical recommendation systems output their results either as predictions, a numerical ranking value corresponding to a particular item or recommendations, a list of relevant items .The conventional approaches to computing similarity involve the use of two popular techniques:Pearson correlation& Cosine Equation
We used Jaccard`s coefficient in our models.
3.4 Technologies
The technologies being used to build the system are cutting and largely open source. Open source technologies help in keeping the costs in check, thus enabling the various establishments to use this setup without any cause of concerns with regards to the costs involved.
Amazon Web Services has a very effective and inexpensive service known as Elastic Cloud Compute which allows one to setup servers on the fly with the specifications one requires. We will be using the same (or similar) service to keep the costs down and maintain scalability.
The server uses software known Nginx. Nginx [engine x] is an HTTP and reverse proxy server, as well as a mail proxy server, written by Igor Sysoev. For a long time,it has been running on many heavily loaded Russian sites including Yandex, Mail.Ru,VKontakte,and Rambler.According to Netcraft nginx served or proxied 12.81%busiest sites in February 2013.
The database uses the ever popular MySQL as its DBMS. The web services mentioned earlier are powered by a web framework called Symfony that uses PHP and an ORM called Doctrine. OAuth and SSL can and will be used to implement security.
The tablets supplied to the patrons will be running Android OS.
3.5 Comparison of Existing vs. Proposed systems
3.6 Feature Overview
In this section we won’t go into the detailed features of the system, but instead take a bird’s eye look at the same.
3.6.1 Intuitive, Beautiful& User friendly
The end users, i.e. the restaurant customers, will have maximum interaction with this system.This interaction will mostly occur through the tablet application.
Unlike most applications that have a targeted user base, our application will be used by all & sundry. It could be used by an 8 year old or an 80 year old person, from varying cultures and background. This ensures the need for a design that is intuitive, user friendly and beautiful thus making it a pleasure to use.
3.6.2 Scalable
The system has the potential of being used by millions of users at any given time, with possible peaks at various rush hours at the establishments this may be implemented at. Thus, the system will have to be scalable and should be able to accommodate as many users and as much data as required.
3.6.3 Secure
Since private information about so many businesses and individuals will be stored in our database.We will need to ensure that the whole system - the web service, the app, the database as well as the server be secure from external as well as internal threats.
3.7 Assumptions
The success of the proposed system is based on the following assumptions that:
1. End users should be able to use tablets.
work connection will always remain stable.
3.Security of server is maintained.
4.Tablets should work fault free.
3.8 Risks
The above mentioned assumptions, give rise to a bunch of risks. Those risks are mainly the following:
1.Intrusion of confidential data.
2.Server goes down.
3.Tablets suffer a defect.
4.End users are not able to use the tablet devices.
3.9 Application Demos.
Fig 3.3Login screen of Android app
Fig3.4Browsing Menu
Fig 3.5Add items to order
Fig3.6Order received at kitchen
Fig 3.6 depicts the kitchen interface running in the latest version of the Chrome browser. The main aim of the kitchen frontend is to display the latest orders, place by the customers using the patron frontend, in a comprehensive manner. An AJAX query is fired at a pre-defined regular interval to check if any new orders have been placed and the same is displayed.
This is really not possible in the stand alone systems that currently exist.
A high-quality service system should be customer-centric, i.e., it should immediately recognize the identities,favorite meals and expenditure records of
customers so as to provide customer-centric services. Therefore,using advanced technologies to improve service quality has attracted much attention in recent years.
In recent years, various product recommendation systems have been developed to enhance customer satisfaction and perceived value. Defined as a system which recommends an appropriate product or service after learning the customers’preferences and desires, recommendation systems are powerful tools that allow companies to present personalized offers to their customers.Extracting users’preferences through their buying behaviors and histories of purchased products is the most important element of such a system. The mobile device-based service unit enables instant transmission of customer orders via Internet to the kitchen for meal preparation. In addition, the expenditure information can be sent to the cashier for bill pre-processing. The restaurant managers can access the database to evaluate the business status anytime and make appropriate redeployments for food materials. Notably,all ordering and expenditure information is digitized for database storage, which allows restaurant owners to consider discounts or promotion to customers based on expenditure statistics.Customers can thus appreciate high-quality service, which in turn highly promotes enterprise image and increases business revenue for the restaurant.
3.10 Advantages
Most,if not all, of the current tablet based menu systems use Apple’s iPad device. This is mostly due to the fact that the current generation of iPads were the first tablet devices to feature high resolution displays and the perceived brand recognition and status of the brand that is Apple.
It is a well-known fact that Apple devices sell at a premium.So, if an establishment was to implement this system, they would unfortunately have to pass on the extra costs to their customers that they incurred in the procurement of these devices. High resolution Android tablets are now available at extremely economical rates with varying feature sets. Since all our application needs is a WIFI connection
and good display, the costs of the tablet devices can be kept to a minimum.The centralized servers can allow the sharing of customer data between restaurants, if allowed to do so explicitly by both the customer and the business, and then this can help in providing better recommendations and user experience to the patrons.
基于Android系统的电子菜单软件摘要：在餐厅里，简单易用的菜单能使点菜变得便利。

平板电脑菜单完全改变了顾客的就餐体验。

现有的程序提供了能让餐厅把菜单放到基于IOS和Android的设备上使用的应用，使得用餐者在菜单上能浏览、点菜和刷卡。

我们这里旨在为餐厅提供平板菜单软件，它将基于推荐算法推荐菜式，目前该算法
在别处尚未使用过。

此外，该应用程序是运行在Android 设备上，而不是更昂
贵的IOS设备。

我们使用一个基于云计算的服务器来存储数据库，这使它更加
廉价和安全。

关键词：推荐，平板电脑，菜单，智能，Android 应用，餐厅。

1. 简介
多年来，技术已经极大改变了餐饮业。

但是大部分的创新都是销售点(POS)方面的。

然而，餐厅的其他方面的创新已经成熟，如菜单。

传统的餐饮服务要求服务员与客户在处理他们的订单前直接互动。

但是，一个高质量的推荐服务系统将主动识别顾客、他们最喜爱的食物和支付记录。

有句名言说：“很多人靠眼睛决定吃的”。

电子菜单能提供的信息比传统的纸质菜单更多。

设备消除一些人为的点错单的问题，减少厨房的混乱，因为现在一切都写得很清楚。

使用电子菜单的客人比不使用的客人能节省20％的时间。

众多形象生动的图片能让你更快找到你想要的。

更方便快捷。

可以说电子菜单节省了成本，且客户更多地参与到这个过程中。

餐厅还可以建立自己的电子商务社区。

菜单从原来的单调古板发展到了今天的多姿多彩。

随着电子设备和人性化的触摸屏技术的发展，菜单可以转移到一个全新的平台上面。

有了这个电子菜单，订单可以在第一时间被确定。

不需要到处跑，因为终端盒服务器是连在一起的。

每一个订单是与餐桌上的一个单独的座位关联的，并是在第一时间建立的，就像在纸上，但更精度更大。

资料也可以很容易地共享到整个表，移动或修改，并列出，成本可以实时计算。

推荐算法基于顾客以前的菜单。

这让顾客很快就能找到自己想要的菜，还可以查看最受欢迎的菜肴。

此外，过滤器可以筛选，例如个人喜好，使用价格，口味，数量等。

在孟买，有几家餐馆已经用数字化平板菜单取代了传统的纸质菜单。

但没有的应用程序，顾客只能直接到厨房下定单。

由于受到这个限制，它只是简单观看的菜单。

2. 现有系统
2.1 常规系统
餐厅服务，如预订，处理订单，送餐一般要求服务员记录客户信息，然后发送订单到厨房准备餐点。

顾客埋单时，由收银员计算并收钱。

虽然这个过程很简单，它可能会显着增加服务员的工作量，甚至导致订餐错误或客户优先次序混乱，尤其是在客户数量突然增加的繁忙时间，这会严重降低整体服务质量。

2.2 电子POS终端
一个普遍使用的系统，目前正被世界各地众多的餐馆和连锁店使用着，是一个电子销售点终端系统。

这里，由服务器/服务员接受订单和提交到终端，并由终端把订单提交到计算机。

该订单会通过网络发送到厨房终端，或者它甚至可能由服务器直接交付到厨房。

虽然相对于纸和笔来说，它是个很大的进步且在世界上得到广泛的使用，但这不会给客户带来太多的好感和只是有益于餐厅管理和收入。

2.3 平板式菜单
随着触摸设备的流行，尤其是苹果iPad，使平板式菜单渐渐失去市场竞争力。

这些平板式菜单只是一个运行在设备上的纸质菜单而已。

从工作流程上来说，就算用了平板式菜单，也还是和前面2.1节的流程一样或者是结合了前面两节的流程。

这对顾客和员工来说是毫无新意的。

并没有完全用到设备最好的功能。

3. 推荐系统
3.1 系统架构
我们的系统的基本体系结构如图3.1所示。

通过这个图就可以很直观的了
解我们的系统。

图3.1基本的系统架构和系统组件
该系统有以下几部分：由Web服务器和数据库组成的终端和由客户端、管
理端、厨房端组成的前端。

该系统是基于比较常用的MVC 模型。

MVC 是非常常用的模型，很受欢迎，易于修改和测试。

前端并不是直接与数据库“对话”。

它们通过静态网络技术来访问数据库。

图3.2流程
图3.2是应用程序的流程图。

3.2 系统概述
这个系统最重要的部分是数据库和顾客端或应用程序端。

提供有价值的东西给餐厅和它的顾客是一个重要的目标，我们相信这会一一实现。

依照这个信念，软件的使用者会被给予大量的重视。

顾客要有一个能运行Android系统的平板电脑。

这个电脑要能与我们云服
务器上的数据库同步。

同步的菜单的数据会保存在设备里，这样顾客就不用每次都等待数据的下载。

顾客也就可以更快地查看菜单。

然后顾客就可以浏览菜单上他们所喜欢的，菜单上能显示的各种分类如价格，人气，评级等。

用户也可以通过点击查看更多信息来查看餐厅提供的其他信息如营养，成分，由来和其他的一些内容。

顾客还可以查看他们可能会喜欢的个性化推荐项目。

这是我们系统最重要的一个部分，不仅提升了客户的体验，还可以提高业务收入。

在浏览菜单时，顾客可将项目添加到他/她的订单里。

这个过程通常被称为“下单”。

在顾客建立、查看、确定订单后。

工作人员马上就能收到订单，并开始工作。

如果条件允许，顾客甚至可以跟踪他们订单的状态，知道他们要的食物和饮料什么时候能送到。

3.3 建议概述
我们可以把参考算法的一些比较新的东西用在我们的菜单软件里。

当大多数平板菜单只能为顾客提供一个简单的菜单时，我们的系统能提供建议，这些建议是基于其他顾客以前所点的菜或相似的各种菜，这能让顾客更快地做出选择。

我们最终决定使用下面的，在一项较早的研究中讨论过方法。

该算法主要有5个部分：
1、用户- 一定数量的人与特定的食品项目的比率。

2、实体- 食品项目。

3、分类- 食品其他信息，如类别价格，质量，是否肉类等。

4、喜好- 根据顾客喜好推荐菜肴。

5、选择- 在加权平均数的基础上由设定好的价值维度形成。

要想每个顾客与食品项目的比率为1至5，就要考虑两个方面：
1、顾客的理想价值维度。

2、这个价值维度的重要性。

录入所有食品的信息后，我们的下一个任务是给它们分类。

我们建立一个分类，然后把所有这类食物放到这个分类里。

顾客找到他所想要的分类后，程
序会只显示这个分类的食物，而过滤掉其他分类的食物。

我们进一步分析各种对食品之间区别。

Jaccard指数可以用来估算相似度。

Jaccard指数也被称为Jaccard相似系数，是一种用于比较样本集相似性和多样性的统计量。

Jaccard系数用于测样本之间的相似度：
基于协同过滤的推荐系统的信息来自于社会上的用户，如意见或评级，从而形成推荐名单。

现有的协同过滤技术包括生成用户项目的矩阵，能从一个大的数据集里找出用户感兴趣的相关的项目。

这涉及到用机器学习技术来调整数据集。

集群包括把数据集分散到特定的集合,其中每个对应于特定的不同标准。

此外，推荐系统除了输出的推荐项目，还能给它们排名。

计算相似性的传统方法用了两种常用的技术：Pearson相关和余弦公式。

在我们的模型中，我们使用的是Jaccard系数。

3.4 技术
用来构建系统的技术是模块化的，开源的。

开源技术有助于成本控制，它不需要额外的费用。

亚马逊网络服务中有一个非常有效和廉价的服务被称为弹性云计算，它允许建立一个合乎规范的服务器。

我们将使用相同服务来保持低成本和维护的简便性。

服务器使用知名软件Nginx。

Nginx 基于HTTP 协议和反向代理服务器，就像一个由Igor Sysoev写邮件代理服务器。

在很久以前，它就已经运行了很多大型的俄罗斯网站，包括Yandex、Mail.Ru、VKontakte和Rambler。

数据库使用日益流行的MySQL作为为数据库管理系统。

前面提到的网络服务由一个被称为Symfony 的使用PHP 和ORM的web 框架提供支持。

OAuth 和SSL将用于保障安全。

提供给顾客的程序是运行在Android 系统的。

3.5 现有的系统与推荐的系统比较
表3.1 系统比较
现有的系统推荐的系统
3.6 功能概述
在本节中，我们将不详细介绍系统的功能，而是从宏观角度来介绍这个软件。

3.6.1 直观，优美和人性化
最终用户，即顾客，通过这个系统能有最大限度的互动。

这种互动将主要是通过平板电脑的应用程序产生。

这个软件不像其他的大多数应用，有针对的用户群，我们的应用程序可以被所有人使用。

它可以被8岁到80 岁的人使用，不管他们的文化和背景是怎么的。

这就必须确保设计是直观，美丽和人性化的，才能让它更好用。

3.6.2 可扩展
系统会被众多用户在不同的时间内使用，特别是就餐高峰期。

因此，该系统将必须是可扩展的，应该能根据需要够容纳尽可能多的用户和尽可能多的数据。

3.6.3 安全
因为很多企业和个人的私人信息将存储在我们的数据库中。

我们需要确保整个系统的web 服务，应用程序，数据库以及服务器是安全的，能够抵御从外部和内部产生的威胁。

3.7 设想
系统的成功使用是基于以下假设：
1、最终用户应该会使用平板电脑。

2、网络连接始终保持稳定。

3、保持服务器的安全性。

4、设备能无故障工作。

3.8 风险
上面提到的假设，会存在风险。

这些风险主要有以下几种：
1、机密数据会泄露。

2、服务器停机。

3、设备有缺陷。

4、最终用户不会使用的平板设备。

3.9 应用演示
图3.3Android应用程序的登录界面
图3.4浏览菜单
图3.5添加项目
图3.6厨房接受订单
图3.6描述了厨房端运行在最新版Chrome 浏览器上的情况。

厨房端主要是全面地显示从客户端发来的最新订单。

AJAX 查询是一个预先设定的正区间，用来检查是否有任何新订单加入和实时显示。

这是不可能在单机系统里存在的。

高品质的服务体系应该以顾客为中心的，也就是说，它应该能立即辨认出顾客的身份，喜欢的食物，支出记录，从而为顾客提供贴心的服务。

因此，采用先进的技术来提高服务质量的话题，近年来备受关注。

近年，各种产品推荐系统被开发来提高客户满意度和认可度。

强大的推荐
系统能记住顾客偏好和需求，使得公司可以向他们的客户提供个性化服务。

系
统可以从顾客的购买行为和购买产品的历史中提取用户的喜好，这是系统最重
要的元素。

基于移动设备顾客的订单通过互联网实现即时传输到厨房准备餐点。

此外，所有的订购和支出信息可被数据库存储，它让餐馆老板能根据顾客的支
出来考虑折扣或促销。

客户可以因此享受高质量的服务，且餐厅的形象得到提升，同时餐厅的收入也能提高。

3.10 优势
目前大多数的菜单软件是运行在苹果公司的iPad设备上的。

这主要是因为ipad具有分辨率很高的显示器和很高的知名度。

但是苹果设备的价格过高是个不争的事实。

如果我们选择苹果设备，那么顾客就要承担额外的成本，而价格相对低廉安卓设备也达到我们的目的。

由于我们的应用程序只需要一个WiFi 连接和良好的显示就可以了，所以成本可以保持到最低。

集中服务器可以让各个餐厅共享顾客的数据，如果顾客和其他餐厅
同意的话，然后软件还能帮助用户给食客提供更好的建议和体验。