android点菜软件外文翻译--基于安卓系统的电子菜单软件(编辑修改稿)

android点菜软件外文翻译--基于安卓系统的电子菜单软件(编辑修改稿)内容摘要：

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 of 1 to 5 with respect to two things: 1. User’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 fooditems by the customers, we apply normalization to the retrieved list of fooditems, to filter out redundant clicks. We further investigate the levels of monality that existed between various pairs of food items. Jaccard’s coefficient was used to calculate the degree of similarity. The Jaccard index, also known as the Jaccard similarity coefficient (originally coined coefficient de munaute by Paul Jaccard), is a statistic used for paring 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 filteringbased remender systems rely on information derived from social activities of the users, such as opinions or ratings, to form predictions or produce remendation lists. Existing collaborative filtering techniques involve generating a useritem matrix, from which remendation results could be derived Contentbased filtering focuses on the selection of relevant items from a Contentbased 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 remendation systems output their results either as predictions, a numerical ranking value corresponding to a particular item or remendations, a list of relevant items .The conventional approaches to puting similarity involve the use of two popular techniques: Pearson correlation amp。 Cosine Equation We used Jaccard`s coefficient in our models. 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, , VKontakte, and Rambler. According to Netcraft nginx served or proxied % busiest sites in February 2020. 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. Comparison of Existing vs. Proposed systems Table Comparison of Systems Existing System Proposed System Operating Systems IOS Android Communication Channel Communication between the customer amp。 the waiter Communication between customer amp。 the terminal Customer identification RFID used to identify customers Additional hardware required User accounts maintained in the database server Server location Localized server Centralized server Remendation System Remendations not implemented Remendations implemented Exclusivity Exclusive to every establishment Can be extended to be used by multiple establishments 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. Intuitive, Beautiful amp。 User friendly The end users, . 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 amp。 sundry. It co。