本文關(guān)鍵詞：基于Android的移動(dòng)學(xué)習(xí)交流平臺(tái)的設(shè)計(jì)與實(shí)現(xiàn)　出處：《河北科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 移動(dòng)學(xué)習(xí) 推薦算法 在線學(xué)習(xí) 在線交流 Android 聚類

【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)技術(shù)的不斷發(fā)展,智能手機(jī)的不斷普及,現(xiàn)在越來越多的人通過手機(jī)等智能設(shè)備來進(jìn)行學(xué)習(xí)和交流。為了滿足教師和學(xué)生實(shí)時(shí)的溝通交流,提高學(xué)生的學(xué)習(xí)興趣和效率,本文設(shè)計(jì)了基于Android的移動(dòng)學(xué)習(xí)交流系統(tǒng)。本文首先通過Openfire完成了客戶端與服務(wù)器端的通信,經(jīng)過服務(wù)器的配置和客戶端的開發(fā),實(shí)現(xiàn)了用戶之間的互動(dòng)交流。其次通過對(duì)用戶的需求分析,總結(jié)出了系統(tǒng)的功能模塊,根據(jù)軟件工程的方法實(shí)現(xiàn)了系統(tǒng)的功能模塊的開發(fā)。另外一方面,為了提高用戶體驗(yàn)和系統(tǒng)的友好性,本文對(duì)協(xié)同推薦算法進(jìn)行了研究。通過采用數(shù)據(jù)庫(kù)記錄和web日志相結(jié)合的方法,對(duì)用戶的瀏覽記錄和行為進(jìn)行挖掘,從而將這些隱性的用戶行為轉(zhuǎn)化為顯性的用戶對(duì)項(xiàng)目評(píng)價(jià);接著通過余弦相似度計(jì)算方法來計(jì)算用戶之間的相似度;然后通過K-means類聚方法對(duì)用戶進(jìn)行聚類,從而根據(jù)他們的行為把用戶自動(dòng)地劃分為若干個(gè)用戶聚類簇;最后通過用戶對(duì)最近鄰居的評(píng)分來預(yù)測(cè)其對(duì)目標(biāo)項(xiàng)目的評(píng)分,從而進(jìn)行推薦�；谏鲜龇椒�,實(shí)現(xiàn)了基于Android的移動(dòng)學(xué)習(xí)交流系統(tǒng),系統(tǒng)基本滿足了用戶的功能需求。通過開發(fā)本系統(tǒng)不僅促進(jìn)了學(xué)生之間的相互交流,而且方便了學(xué)生的學(xué)習(xí),對(duì)于他們學(xué)習(xí)成績(jī)的提高有一定的促進(jìn)作用。
[Abstract]:With the continuous development of mobile Internet technology and the popularity of smart phones, more and more people are learning and communicating through smart devices such as mobile phones, in order to meet the real-time communication between teachers and students. To improve students' learning interest and efficiency, this paper designs a mobile learning communication system based on Android. Firstly, the communication between client and server is completed through Openfire. Through the configuration of the server and the development of the client, the interactive communication between users is realized. Secondly, the function module of the system is summarized by analyzing the needs of the users. According to the method of software engineering, the function module of the system is developed. On the other hand, in order to improve the user experience and the friendliness of the system. In this paper, the collaborative recommendation algorithm is studied. By using the combination of database records and web logs, the browsing records and behaviors of users are mined. In order to transform these implicit user behaviors into explicit user evaluation of the project; Then, the similarity between users is calculated by the method of cosine similarity. Then, the users are clustered by K-means clustering method, and the users are automatically divided into several user clusters according to their behavior. Finally, the user's score on the nearest neighbor is predicted to recommend the target items. Based on the above method, a mobile learning communication system based on Android is implemented. The system meets the functional needs of users basically. Through the development of the system not only to promote the exchange of students, but also to facilitate the study of students, to improve their academic performance has a certain role.
【學(xué)位授予單位】：河北科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP311.52;TP316

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