【正文】 eature that we aim to include in our menu. When most tablet menus provide the customers with only a simple menu, this system will provide remendations which will make it easier to build an order considering what other customers have ordered previously or the similarities between various dishes. Remendation systems using sets were considered . We finally decided to use the below methodology, which has been discussed in an earlier study. The algorithm mainly has 5 parts: 1. Users 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 . Price, quality, meat content, etc. 4. Belief System is personal to each user amp。 workshop on Advanced Computing 2020 (ICWAC 2020) 中文 4190字 Intelligent eRestaurant 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 pletely revolutionizes the patron’s dining experience. Existing programs provide an app that restaurants can use to feed their menus into IOS amp。 Android based tablets and make it easier for the diners to flip, swipe amp。 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 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 ?ltering focuses on the selection of relevant items from a Contentbased ?ltering 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。 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。我們這里旨在為餐廳提供平板菜單軟件，它將基于推薦算法推薦菜式，目前該算法在別處尚未使用過。 1. 簡介 多年來，技術(shù)已經(jīng)極大改變了餐飲業(yè)。但是，一個(gè)高質(zhì)量的推薦服務(wù)系統(tǒng)將主動(dòng)識(shí)別顧客、他們最喜愛的食物和支付記錄。使用電子菜單的客人比不使用的客人能節(jié)省 20％的時(shí)間。餐廳還可以建立自己的電子商務(wù)社區(qū)。不需要到處跑，因?yàn)榻K端盒服務(wù)器是連在一起的。這讓顧客很快就能找到自己想要的菜，還可以查看最受歡迎的菜肴。由于受到這個(gè)限制，它只是簡單觀看的菜單。 電子 POS終端 一個(gè) 普遍使用的系統(tǒng)，目前正被世界各地眾多的餐館和連鎖店使用著，是一個(gè)電子銷售點(diǎn)終端系統(tǒng)。 平板式菜單 隨著觸摸設(shè)備的流行，尤其是蘋果 iPad，使平板式菜單漸漸失去市場(chǎng)競爭力。并沒有完全用到設(shè)備最好的功能。 該系統(tǒng)是基于比較常用的 MVC 模型。 圖 流程 圖 。 顧客要有一個(gè)能運(yùn)行 Android系統(tǒng)的平板電腦。 然后顧客就可以瀏覽菜單上他們所喜歡的，菜單上能顯示的各種分類如價(jià)格，人氣，評(píng)級(jí)等。 在瀏覽菜單時(shí)，顧客可將項(xiàng)目添加到他 /她的訂單里。 如果條件允許，顧客甚至可以跟蹤他們訂單的狀態(tài)，知道他們要的食物和飲料什么時(shí)候能送到。 該算法主要有 5個(gè)部分： 用戶 一定數(shù)量的人與特定的食品項(xiàng)目的比率。 選擇 在加權(quán)平均數(shù)的基礎(chǔ)上由設(shè)定好的價(jià)值維度形成。我們建立一個(gè)分類，然后把所有這類食物放到這個(gè)分類里。 Jaccard指數(shù)也被稱為 Jaccard相似系數(shù)，是一種用于比較樣本集相似性和多樣性的統(tǒng)計(jì)量。集群包括把數(shù)據(jù)集分散到特定的集合 ,其中每個(gè)對(duì)應(yīng)于特定的不同標(biāo)準(zhǔn)。 技術(shù) 用來構(gòu)建系統(tǒng)的技術(shù)是模塊化的，開源的。 服務(wù)器使用知名軟件 Nginx。前面提到的網(wǎng)絡(luò)服務(wù)由一個(gè)被稱為 Symfony的使用 PHP和 ORM的