【摘要】：互聯(lián)網(wǎng)使用的增長直接催生了信息過載。大量可用的話題出現(xiàn)在網(wǎng)上使得相關(guān)信息源的搜索變得困難和耗時。在線教育是傳統(tǒng)教育模式的一種變革,用戶可以根據(jù)自己的學(xué)習(xí)節(jié)奏在虛擬環(huán)境中學(xué)習(xí),但由于當(dāng)前的搜索引擎和教育網(wǎng)絡(luò)很難在短時間內(nèi)為知識的搜尋者提供他們真正需要的資源,資源獲取極其耗時成了一個大問題。另外,目前的在線教育交互性差,偏向于快速適應(yīng)和趣味性,也使得用戶的忠誠度非常低。 當(dāng)前環(huán)境下,很多研究人員通過引入社會網(wǎng)絡(luò)給用戶推薦與他們的選擇和歷史行為相一致的資源,其中利用到協(xié)同過濾,基于內(nèi)容的過濾以及混合推薦算法。早期的方法強調(diào)為特定用戶計算基于內(nèi)容相關(guān)度的重要性,系統(tǒng)為用戶提供相關(guān)度得分最高的資源給用戶。評分計算方法圍繞內(nèi)容的多個屬性計算評分,不同的內(nèi)容相關(guān)度評分計算結(jié)果差異較大。例如,社會網(wǎng)絡(luò)中認為用戶的親密度和歷史瀏覽行為模式在計算評分中更為重要,而在一般的推薦系統(tǒng)中,更側(cè)重于有共同屬性的相似用戶,如位置、教育程度、隱私設(shè)置等。推薦系統(tǒng)中一個最主要的問題是冷啟動,數(shù)據(jù)缺失或者稀疏數(shù)據(jù)成為推薦系統(tǒng)的瓶頸。 本文在現(xiàn)有的社會網(wǎng)絡(luò)的基礎(chǔ)上提出了一種協(xié)作、共享、排名和推薦的在線教育系統(tǒng),根據(jù)用戶的個人偏好進行相應(yīng)的推薦。系統(tǒng)首先通過用戶注冊模塊獲取用戶的相關(guān)信息,具有相同偏好的相似用戶被分到同一個組中。然后基于相似用戶群組,計算出資源的相關(guān)性得分,把得分最高的資源推薦給用戶。針對推薦系統(tǒng)的冷啟動問題,也就是說,如果用戶沒有設(shè)置相應(yīng)的偏好信息,系統(tǒng)則給用戶推薦最近熱門的資源并從用戶的反饋行為中學(xué)習(xí)。在這樣系統(tǒng)中,用戶接口設(shè)計是極其重要的部分,可以說是整個系統(tǒng)的重心,用戶體驗良好與否直接影響整個系統(tǒng)的成敗。系統(tǒng)的質(zhì)量取決于用戶界面的易使用、易理解、易交互,并且用戶界面根據(jù)用戶需求是完全可以定制的。通過這樣的方式設(shè)計系統(tǒng),可以減少每個資源遍歷的負載,提高用戶的效率,并得出最合適的結(jié)果。綜合考慮用戶當(dāng)前的搜索內(nèi)容和用戶偏好使得資源的搜索更加高效,這是當(dāng)前在線學(xué)習(xí)模式的變革,也是未來在線教育的一個關(guān)鍵技術(shù)點。圖35副,表5個,參考文獻60篇。
[Abstract]:The growth of Internet use directly spawned information overload. The availability of a large number of topics on the Internet makes it difficult and time-consuming to search for relevant sources of information. Online education is a kind of transformation of traditional education mode. Users can learn in virtual environment according to their own learning rhythm. However, because the current search engines and educational networks are difficult to provide the knowledge searchers with the resources they really need in a short period of time, obtaining resources becomes a big problem. In addition, current online education is less interactive, more adaptive and more interesting, making users' loyalty very low. In the current environment, many researchers introduce social networks to recommend resources consistent with their choices and historical behaviors, which make use of collaborative filtering, content-based filtering and hybrid recommendation algorithms. Early methods emphasized the importance of computing content-based relevance for specific users, and the system provided users with resources with the highest relevance score. The method of score calculation is based on the multiple attributes of content, and the results of different content correlation scores are quite different. For example, in social networks, user affinity and historical browsing behavior patterns are considered to be more important in the calculation of ratings, while in general recommendation systems, similar users with common attributes, such as location, educational level, privacy settings, etc. One of the most important problems in recommendation system is cold start. Missing or sparse data becomes the bottleneck of recommendation system. Based on the existing social networks, this paper proposes an online education system based on collaboration, sharing, ranking and recommendation, which can be recommended according to users' personal preferences. The system first obtains the relevant information of the user through the user registration module, and similar users with the same preference are divided into the same group. Then, based on the similar user groups, the correlation score of resources is calculated, and the resources with the highest score are recommended to the users. For the cold start problem of recommendation system, that is, if the user does not set the corresponding preference information, the system recommends the most popular resources to the user and learns from the feedback behavior of the user. In such a system, user interface design is an extremely important part, can be said to be the focus of the whole system, the user experience is good or not directly affect the success or failure of the whole system. The quality of the system depends on the user interface is easy to use, easy to understand, easy to interact, and the user interface can be completely customized according to user needs. By designing the system in this way, the load of each resource traversal can be reduced, the efficiency of the user can be improved, and the most suitable result can be obtained. The comprehensive consideration of users' current search content and user preferences makes the search of resources more efficient, which is the change of current online learning mode and a key technology point of online education in the future. There are 35 pairs of figs, 5 tables and 60 references.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.3

【共引文獻】

相關(guān)期刊論文 前10條

1 黎星星;Building a better recommender system in E-commerce[J];Journal of Chongqing University;2003年01期

2 朱郁筱;呂琳媛;;推薦系統(tǒng)評價指標(biāo)綜述[J];電子科技大學(xué)學(xué)報;2012年02期

3 侯治平;;用戶行為模式下電子商務(wù)網(wǎng)站個性化推薦研究[J];電腦與信息技術(shù);2011年04期

4 谷鈺;薛國明;;基于ACM在線評測推薦系統(tǒng)模型研究[J];電腦知識與技術(shù);2011年07期

5 劉兆興;張寧;李季明;;基于協(xié)同過濾和網(wǎng)絡(luò)結(jié)構(gòu)的個性化推薦算法[J];復(fù)雜系統(tǒng)與復(fù)雜性科學(xué);2011年02期

6 劉鵬程;畢旭;羅靜;朱麗霞;;百度地圖API路網(wǎng)搜索功能在職住分離研究中的應(yīng)用[J];地理空間信息;2013年05期

7 李汶華;熊曉棟;郭均鵬;;一種基于案例推理和協(xié)商的群體推薦算法[J];系統(tǒng)工程;2013年11期

8 劉平峰;聶規(guī)劃;陳冬林;;電子商務(wù)推薦系統(tǒng)中推薦策略的自適應(yīng)性[J];計算機工程與應(yīng)用;2007年04期

9 劉平峰;聶規(guī)劃;陳冬林;;基于知識的電子商務(wù)智能推薦系統(tǒng)平臺設(shè)計[J];計算機工程與應(yīng)用;2007年19期

10 王進;;一種使用DSmTrust信任模型的推薦系統(tǒng)[J];計算機工程與應(yīng)用;2011年13期

相關(guān)博士學(xué)位論文 前10條

1 張寅;個性化技術(shù)及其在數(shù)字圖書館中應(yīng)用的研究[D];浙江大學(xué);2009年

2 陳偉;基于時序文本挖掘的新聞內(nèi)容理解與推薦技術(shù)研究[D];浙江大學(xué);2010年

3 王宏宇;商務(wù)推薦系統(tǒng)的設(shè)計研究[D];中國科學(xué)技術(shù)大學(xué);2007年

4 余小高;電子商務(wù)環(huán)境中分布式數(shù)據(jù)挖掘的研究[D];武漢理工大學(xué);2007年

5 劉平峰;基于知識網(wǎng)格的電子商務(wù)智能推薦理論方法研究[D];武漢理工大學(xué);2006年

6 劉康苗;自適應(yīng)網(wǎng)絡(luò)信息獲取服務(wù)技術(shù)研究[D];浙江大學(xué);2008年

7 史e，

本文編號：2209366