【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的發(fā)展,當(dāng)今社會早已進入信息爆炸的時代,人們?nèi)找嬖鲩L的知識需求也已經(jīng)超出傳統(tǒng)教育模式所能滿足的范圍。如何在信息世界里充分利用信息資源,營造個性化學(xué)習(xí)環(huán)境,滿足人們隨時隨地學(xué)習(xí)的需求,成了當(dāng)務(wù)之急。社會學(xué)習(xí)網(wǎng)絡(luò)是基于Web2.0技術(shù)和大數(shù)據(jù)挖掘技術(shù)所構(gòu)建的用于知識發(fā)掘、整合、存儲以及傳播的網(wǎng)絡(luò),能夠根據(jù)用戶信息提供個性化學(xué)習(xí)方案,并通過文本、語音、視頻等人機交互方式將知識提供給用戶,滿足人們隨時隨地學(xué)習(xí)的需求。社會學(xué)習(xí)網(wǎng)絡(luò)雖然能夠滿足人們在信息爆炸時代的學(xué)習(xí)需求,但是由于仍處于發(fā)展初期,其智能決策機制還遠(yuǎn)不夠完善。論文基于移動互聯(lián)網(wǎng)和大數(shù)據(jù)挖掘技術(shù)的發(fā)展,研究分類方法在社會學(xué)習(xí)網(wǎng)絡(luò)中的設(shè)計與應(yīng)用,主要研究工作和創(chuàng)新點包括以下方面:1)為了獲取真實可靠的研究數(shù)據(jù),論文首先設(shè)計開發(fā)了一個在線社會學(xué)習(xí)互聯(lián)網(wǎng)平臺。與其他在線學(xué)習(xí)平臺不同,該平臺不僅提供了課程討論、教學(xué)視頻播放等學(xué)習(xí)功能,還提供信息引導(dǎo)、校園互動等社交功能,為社會學(xué)習(xí)網(wǎng)絡(luò)的平臺設(shè)計與實現(xiàn)提供了參考方案。作為分類方法研究的平臺基礎(chǔ),該平臺實現(xiàn)了社會學(xué)習(xí)網(wǎng)絡(luò)的數(shù)據(jù)采集功能。2)社會學(xué)習(xí)網(wǎng)絡(luò)的課程討論區(qū)用于用戶之間的交流,但目前存在著信息泛濫、雜亂的現(xiàn)象,給用戶帶來極大的不便。針對這一問題,論文利用數(shù)據(jù)挖掘技術(shù),提出了一種課程討論區(qū)信息分類與排序方法,幫助用戶快速獲取有用信息。與單門課程分類方法不同,論文利用n門課程的主題信息組成數(shù)據(jù)源,將二元不平衡分類問題轉(zhuǎn)換成n+l元平衡分類問題,從而提高課程主題分類與排序的準(zhǔn)確性。實驗結(jié)果驗證了所提主題分類與排序算法的有效性,完善了社會學(xué)習(xí)網(wǎng)絡(luò)的智能機制。3)社會學(xué)習(xí)網(wǎng)絡(luò)保存了大量的用戶視頻點擊信息,但這些信息都沒有得到利用,造成極大的信息浪費。針對這一問題,論文提出了一種基于視頻點擊流數(shù)據(jù)的用戶分類方法,利用用戶點擊行為來識別用戶本身的學(xué)習(xí)程度。與其他視頻點擊行為研究不同,論文基于在線學(xué)習(xí)互聯(lián)網(wǎng)平臺改進用戶點擊事件設(shè)計,建立新的用戶點擊模型,并將用戶點擊行為與學(xué)習(xí)程度聯(lián)系起來。實驗結(jié)果驗證了所提視頻學(xué)習(xí)用戶分類算法的準(zhǔn)確性,完善了社會學(xué)習(xí)網(wǎng)絡(luò)的個性化策略。
[Abstract]:With the development of Internet technology, the society has entered the era of information explosion, and the increasing demand for knowledge has already exceeded the scope of traditional education model. How to make full use of information resources in the information world, to create a personalized learning environment, and to meet the needs of people learning at any time and anywhere has become an urgent task. Social learning network is based on Web2.0 technology and big data mining technology for knowledge mining, integration, storage and dissemination of the network, can provide personalized learning programs according to user information, and through text, voice, Human-computer interaction, such as video, provides users with knowledge to meet the needs of learning anytime and anywhere. Although the social learning network can meet the learning needs of people in the era of information explosion, its intelligent decision-making mechanism is far from perfect because it is still in the early stage of development. Based on the development of mobile Internet and big data mining technology, this paper studies the design and application of classification method in social learning network. The main research work and innovations include the following aspects: 1) in order to obtain real and reliable research data, This paper first designs and develops an online social learning Internet platform. Unlike other online learning platforms, the platform not only provides learning functions such as course discussion, instructional video playback, but also provides social functions such as information guidance, campus interaction, etc. It provides a reference scheme for the design and implementation of social learning network platform. As the basis of classification research, the platform realizes the data collection function of social learning network. 2) the course discussion area of social learning network is used for the communication between users. It brings great inconvenience to users. In order to solve this problem, this paper proposes a method of information classification and sorting in course discussion area by using data mining technology, which can help users to obtain useful information quickly. Different from the single course classification method, the paper uses the topic information of n courses to form a data source, and converts the binary unbalanced classification problem into the n-l element balanced classification problem, thus improving the accuracy of course topic classification and sorting. The experimental results verify the validity of the proposed algorithm and improve the intelligent mechanism of social learning network. 3) Social learning network preserves a large number of user video click information, but these information are not utilized. Cause a great waste of information. In order to solve this problem, a user classification method based on video click-stream data is proposed in this paper. The user click-behavior is used to identify the user's learning degree. Different from other video click-behavior research, this paper improves user click event design based on online learning Internet platform, establishes a new user click model, and links user click behavior with learning level. The experimental results verify the accuracy of the proposed video learning user classification algorithm and improve the personalized strategy of social learning network.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP393.4;TP311.13

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