博碩士論文 103522101 詳細資訊




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姓名 王傑生(CHIEH-SHENG WANG)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 應用大數據分析開發適性化教材推薦系統
(Applying Big Data Analytics to Develop an Adaptive Course Material Recommendation System)
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摘要(中) 近年來開放教育資源(Open Educational Resources)的興起,允許使用者開放
修改及分享OER 教材並提供教師免費或是低成本的教學資源以降低教師提供課
程的門檻,提供更多讓學習者受教的機會。
然而開放式教育資源發展至今,教材數目越來越龐大,導致使用者在為數眾
多的教材中尋找所需的教材時需要花費大量的時間及人力。本研究所應用的開放
教育資源平台同樣也遭遇相同的問題,透過將推薦系統導入該平台雖然可以有效
的減輕教材數目所帶來的影響,然而現有的推薦系統使用內容導向式演算法產生
與教材內容相關的教材推薦雖然可以推薦相關教材,但是卻缺少使用者回饋,修
正教材推薦的結果使推薦結果能更加貼近使用者。
因此,本研究透過蒐集使用者在開放教育資源的瀏覽歷程(Click-stream)作為
推測使用者喜好的參考,為了處理大量的使用者瀏覽歷程資料,本研究使用Spark
資料分析框架作為開發工具,將使用者依照喜好相似程度透過分群演算法建立使
用者族群模型,最後結合瀏覽歷程及使用者族群模型推測使用者的喜好並給予適
性化的教材推薦,使教材推薦結果更符合使用者需求且幫助使用者縮短搜尋教材
的時間。
摘要(英) Open Educational Resources has become popular for serval years. It allows user
to share and revise the open educational resources. By providing no or low cost
educational resources to institutions and educators brings more opportunity to the
students to access to education.
However, with developed for many years the amount of open educational
resources has become more and more huge, it costs a lot of time for users to find the
right educational resource and it becomes difficult for users to find the educational
resources that they really want. This paper find that an open educational resources
platform has the same problem. Although it already has a content-based
recommendation system, the lack of user feedbacks leads to it cannot make the
recommendation result fit to the user requirement.
In this paper, we collect the click-stream from user as the preference of user. In
order to deal with vast amount of data, we use Spark to build user cluster model by
user’s preference. Finally, we combined current user’s preference with user cluster
model and recommended the adaptive educational resource to current user.
關鍵字(中) ★ 開放教育資源
★ 推薦系統
★ 協同過濾
★ Clickstream
關鍵字(英)
論文目次 摘要 ........................................................................................................................... i
ABSTRACT .............................................................................................................. ii
圖目錄 ..................................................................................................................... iv
表格目錄 .................................................................................................................. v
1 緒論 .................................................................................................................. 1
1.1 研究背景與動機 ..................................................................................... 1
1.2 研究目的 ................................................................................................. 2
2 文獻探討 ........................................................................................................... 3
2.1 開放式教育資源(OER) ........................................................................... 3
2.2 瀏覽歷程(Clickstream)............................................................................ 3
2.3 推薦系統 ................................................................................................. 4
3 系統設計 ........................................................................................................... 6
3.1 開發環境與工具 ..................................................................................... 8
3.2 資料收集 ............................................................................................... 10
3.3 資料儲存 ............................................................................................... 13
3.4 資料萃取與分析 ................................................................................... 14
3.5 資料使用 ............................................................................................... 24
4 結果 ................................................................................................................ 28
5 討論與建議 ..................................................................................................... 32
5.1 討論 ...................................................................................................... 32
5.2 建議 ...................................................................................................... 33
參考文獻 ................................................................................................................ 34
參考文獻 Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification : Using
social and content-based information in recommendation, In Proceedings of
the 15th National Conference on, Artificial Intelligence, 714–720.
Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation
coefficient. In Noise reduction in speech processing (pp. 1-4). Springer
Berlin Heidelberg.
Bobadilla, J., Hernando, A., Ortega, F., & Bernal, J. (2011). A framework for
collaborative filtering recommender systems. Expert Systems with
Applications, 38(12), 14609-14623.
Bobadilla, J., Serradilla, F., & Bernal, J. (2010). A new collaborative filtering
metric that improves the behavior of recommender systems. Knowledge-
Based Systems,23, 520–528.
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender
systems survey. Knowledge-Based Systems, 46, 109-132.
Bucklin, R. E., & Sismeiro, C. (2009). Click here for Internet insight: Advances in
clickstream data analysis in marketing. Journal of Interactive Marketing,
23(1), 35-48.
Butcher, N. (2015). A basic guide to open educational resources (OER).
Commonwealth of Learning, Vancouver and UNESCO.
Chatterjee, P., Hoffman, D. L., & Novak, T. P. (2003). Modeling the clickstream:
Implications for web-based advertising efforts. Marketing Science, 22(4),
520-541.
Chen, L., & Su, Q. (2013, July). Discovering user′s interest at E-commerce site
using clickstream data. In 2013 10th International Conference on Service
Systems and Service Management (pp. 124-129). IEEE.
Chen, Q. (2010, August). Use of open educational resources: Challenges and
strategies. In International Conference on Hybrid Learning (pp. 339-351).
Springer Berlin Heidelberg.
Choi, K., Yoo, D., Kim, G., & Suh, Y. (2012). A hybrid online-product
recommendation system: Combining implicit rating-based collaborative
filtering and sequential pattern analysis. Electronic Commerce Research and
Applications, 11(4), 309-317.
35
Claypool, M., Le, P., Wased, M., & Brown, D. (2001, January). Implicit interest
indicators. In Proceedings of the 6th international conference on Intelligent
user interfaces (pp. 33-40). ACM.
Conrad, D., Mackintosh, W., McGreal, R., Murphy, A., & Witthaus, G. (2013).
Report on the assessment and accreditation of learners using OER.
Crespo, R. G., Martínez, O. S., Lovelle, J. M. C., García-Bustelo, B. C. P., Gayo,
J. E. L., & De Pablos, P. O. (2011). Recommendation System based on user
interaction data applied to intelligent electronic books. Computers in Human
Behavior, 27(4), 1445-1449.
Hylén, J. (2006). Open educational resources: Opportunities and
challenges.Proceedings of Open Education, 49-63.
Kim, Y. S., & Yum, B. J. (2011). Recommender system based on click stream
data using association rule mining. Expert Systems with Applications, 38(10),
13320-13327.
Lingras, P., Chen, M., & Miao, D. (2014). Qualitative and quantitative
combinations of crisp and rough clustering schemes using dominance
relations. International Journal of Approximate Reasoning, 55(1), 238-258.
Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: for marketing, sales,
and customer relationship management. John Wiley & Sons.
Liu, N. H., Hsieh, S. J., & Tsai, C. F. (2010). An intelligent music playlist generator
based on the time parameter with artificial neural networks. Expert Systems
with Applications, 37(4), 2815-2825.
Minguillón, J., Rodríguez, M. E., & Conesa, J. (2010, December). Extending
learning objects by means of social networking. In International Conference
on Web-Based Learning (pp. 220-229). Springer Berlin Heidelberg.
Nasraoui, O., Cardona, C., Rojas, C., & Gonzalez, F. (2003, August). Mining
evolving user profiles in noisy web clickstream data with a scalable immune
system clustering algorithm. In Proc. of WebKDD (pp. 71-81).
Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and
classification of recommender systems research. Expert Systems with
Applications, 39(11), 10059-10072.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based
collaborative filtering recommendation algorithms. In Proceedings of the
36
10th international conference on World Wide Web (pp. 285-295). ACM.
Shi, C., Fu, S., Chen, Q., & Qu, H. (2015, April). VisMOOC: Visualizing video
clickstream data from massive open online courses. In 2015 IEEE Pacific
Visualization Symposium (PacificVis) (pp. 159-166). IEEE.
Shinde, S. K., & Kulkarni, U. (2012). Hybrid personalized recommender system
using centering-bunching based clustering algorithm. Expert Systems with
Applications, 39(1), 1381-1387.
Su, Q., & Chen, L. (2015). A method for discovering clusters of e-commerce
interest patterns using click-stream data. Electronic Commerce Research and
Applications, 14(1), 1-13.
Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2008). Providing
justifications in recommender systems. IEEE Transactions on Systems, Man,
and Cybernetics-Part A: Systems and Humans, 38(6), 1262-1272.
Wang, J., De Vries, A. P., & Reinders, M. J. (2006, August). Unifying user-based
and item-based collaborative filtering approaches by similarity fusion.
InProceedings of the 29th annual international ACM SIGIR conference on
Research and development in information retrieval (pp. 501-508). ACM.
Wang, S. L., & Wu, C. Y. (2011). Application of context-aware and personalized
recommendation to implement an adaptive ubiquitous learning
system. Expert Systems with applications, 38(9), 10831-10838.
Wei, J., Shen, Z., Sundaresan, N., & Ma, K. L. (2012, October). Visual cluster
exploration of web clickstream data. In Visual Analytics Science and
Technology (VAST), 2012 IEEE Conference on (pp. 3-12). IEEE.
Wiley, D., Bliss, T. J., & McEwen, M. (2014). Open Educational Resources: a
review of the literature. In Handbook of research on educational
communications and technology (pp. 781-789). Springer New York.
Yuan, L., MacNeill, S., & Kraan, W. G. (2008). Open Educational Resourcesopportunities
and challenges for higher education.
Xie, B., Han, P., Yang, F., Shen, R. M., Zeng, H. J., & Chen, Z. (2007). DCFLA: A
distributed collaborative-filtering neighbor-locating algorithm.Information
Sciences, 177(6), 1349-1363.
Zheng, N., Li, Q., Liao, S., & Zhang, L. (2010). Which photo groups should I
choose? A comparative study of recommendation algorithms in
37
Flickr. Journal of Information Science, 36(6), 733-750.
指導教授 楊鎮華 審核日期 2016-7-22
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