本文選題：大規(guī)模公開線上課程 + 推薦系統(tǒng)��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：得益于互聯(lián)網(wǎng)的高速發(fā)展,傳統(tǒng)教育領(lǐng)域正在發(fā)生翻天覆地的變化。近年來,一種無門檻、費用低廉、學(xué)習(xí)資源豐富的新興教育方式正在普及——MOOC。但隨著MOOC平臺的迅速發(fā)展,MOOC課程數(shù)量大幅度增長,造成信息過載的問題。用戶很難從大量的MOOC課程中選取自己需要的課程,造成“選課難”的問題。因此使用智能算法解決MOOC平臺信息過載問題,幫助用戶選取合適的課程,同時讓優(yōu)秀的課程脫穎而出是很有必要的。推薦系統(tǒng)被認(rèn)為是一種解決信息過載問題更加高效的方法。雖然推薦系統(tǒng)已經(jīng)成功應(yīng)用于很多領(lǐng)域,但是在MOOC領(lǐng)域應(yīng)用推薦系統(tǒng)的國內(nèi)外相關(guān)研究依舊很少。如果直接生搬硬套以往的使用經(jīng)驗,不考慮MOOC應(yīng)用的場景特征,那么課程推薦結(jié)果的準(zhǔn)確率會比較低。為了解決MOOC平臺的“選課難”問題,本文提出了MOOC隱式評分模型,并且根據(jù)當(dāng)下互聯(lián)網(wǎng)大數(shù)據(jù)環(huán)境,設(shè)計實現(xiàn)了一個基于大數(shù)據(jù)平臺的MOOC推薦系統(tǒng)。本文的主要貢獻(xiàn)和創(chuàng)新有:(1)提出MOOC隱式評分模型。該模型根據(jù)MOOC平臺的應(yīng)用場景特征,利用用戶學(xué)習(xí)行為,并借鑒以往推薦系統(tǒng)在其他領(lǐng)域的成功經(jīng)驗。(2)利用MOOC隱式評分模型改進(jìn)了傳統(tǒng)的基于物品的協(xié)同過濾推薦算法和矩陣分解算法。通過實驗結(jié)果證明,使用MOOC隱式評分模型可以提高傳統(tǒng)推薦算法在MOOC應(yīng)用中的推薦準(zhǔn)確率。(3)設(shè)計基于大數(shù)據(jù)平臺的MOOC推薦系統(tǒng)以便于應(yīng)對當(dāng)今互聯(lián)網(wǎng)的大數(shù)據(jù)環(huán)境。該系統(tǒng)根據(jù)大數(shù)據(jù)MOOC應(yīng)用的業(yè)務(wù)特點分為六個模塊,每個模塊都采用微服務(wù)架構(gòu)實現(xiàn),方便系統(tǒng)以后的擴展和維護。(4)利用MapReduce計算模型給出了基于MOOC隱式評分模型的協(xié)同過濾推薦算法的并行化解決方案。然后針對迭代式算法的特點,使用Spark MLlib實現(xiàn)矩陣分解算法,大大減少計算時間和對大規(guī)模數(shù)據(jù)集的處理能力。
[Abstract]:Thanks to the rapid development of the Internet, the traditional field of education is undergoing earth-shaking changes. In recent years, a non-threshold, low-cost, learning resources-rich emerging education is popularizing-MOOC. However, with the rapid development of MOOC platform, the number of MOOC courses has increased greatly, resulting in the problem of information overload. It is difficult for users to choose their own courses from a large number of MOOC courses, which results in the problem of difficult course selection. So it is necessary to use intelligent algorithm to solve the problem of information overload in MOOC platform, to help users select appropriate courses and to make outstanding courses stand out. Recommendation system is considered to be a more efficient way to solve the problem of information overload. Although the recommendation system has been successfully applied in many fields, the research on the application of the recommendation system in the field of MOOC is still few at home and abroad. If the previous experience is directly applied and the scenario features of MOOC application are not considered, the accuracy of the course recommendation results will be low. In order to solve the problem of "difficult course selection" on MOOCs platform, this paper proposes an implicit scoring model of MOOCs, and designs and implements a moc recommendation system based on big data platform according to the current Internet big data environment. The main contributions and innovations of this paper are as follows: (1) an implicit scoring model for MOOC is proposed. According to the characteristics of the application scenarios of MOOC platform, the model utilizes user learning behavior. And draw lessons from the successful experience of the previous recommendation system in other fields. (2) using MOOC implicit scoring model to improve the traditional object-based collaborative filtering recommendation algorithm and matrix decomposition algorithm. The experimental results show that using MOOC-based implicit scoring model can improve the accuracy of traditional recommendation algorithms in MOOC-based applications. (3) the moc recommendation system based on big data platform is designed to deal with the current big data environment of the Internet. The system is divided into six modules according to the service characteristics of the big data MOOC application, each module is implemented by micro-service architecture. It is convenient to extend and maintain the system in the future. (4) the parallel solution of collaborative filtering recommendation algorithm based on MOOC implicit scoring model is presented by using MapReduce computing model. Then, according to the characteristics of iterative algorithm, Spark MLlib is used to implement matrix decomposition algorithm, which greatly reduces the computing time and processing ability of large-scale data sets.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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