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論文中文名稱:以MapReduce為基礎之混合式行動圖書推薦系統 [以論文名稱查詢館藏系統]
論文英文名稱:A Hybrid Mobile Library Book Recommender System Based on MapReduce Techniques [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊管理研究所
畢業學年度:102
出版年度:103
中文姓名:郭大維
英文姓名:Ta-Wei Kuo
研究生學號:101938001
學位類別:碩士
語文別:中文
口試日期:2014-07-01
論文頁數:85
指導教授中文名:翁頌舜
指導教授英文名:Sung-Shun Weng
口試委員中文名:楊欣哲;吳瑞堯;蕭瑞祥
口試委員英文名:Shin-Jer Yang;Rei-Yao Wu;Ruey-Shiang Shaw
中文關鍵詞:協同過濾內容導向方法矩陣分解稀疏性冷啟動MapReduce
英文關鍵詞:Collaborative FilteringContent-based ApproachMatrix FactorizationSparseCold StartMapReduce
論文中文摘要:在推薦系統的方法中,最著名的是協同過濾方法,存在著三個主要問題,分別為冷啟動(Cold Start)、稀疏性(Sparsity)問題及擴充性(Scalability)問題。
本研究結合行動裝置實作出一個圖書館書籍推薦系統,以書籍屬性為基礎的內容導向式方法(Content-based Approach)預測一般使用者對新書籍的評比,利用協同過濾(Collaborative Filtering)相似度計算結合矩陣分解(Matrix Factorization)方法進行運算,其中尚未評分的使用者以使用者輪廓取代評分進行相似度的運算。此外,在使用者間的相似度與矩陣分解運算結合MapReduce演算法,以確保整體系統的準確性及運算時間。
在本研究方法的推薦下,實驗證明推薦結果之MAE評估值為0.9634,較傳統Cosine Measure低,可以達到準確且優良的書籍推薦結果。另外,在MapReduce架構進行相似度計算,運算時間也遠小於傳統迴圈式算法,證明可以有效提升推薦系統中的運算效能。
論文英文摘要:The most famous method in the recommender system, collaborative filtering, leaves three challenges which are the problems of cold-start, sparsity and scalability.
This study has implemented a library book recommender system with mobile devices. We conduct a content-based approach based on book attributes to predict user ratings on new books and use the collaborative filtering approach combining with matrix factorization to calculate similarity between users. When users have no ratings, we use the user profiles for replacement in similarity computing. Furthermore, in the user similarity computing and matrix factorization, we use the MapReduce algorithm to ensure accuracy and performance.
Finally, our study shows that the MAE value of recommendation is less than Cosine Measure’s, and it enhances the result of the book recommendation. In addition, the MapReduce algorithm conducted by this study is shown to improve the performance.
論文目次:中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 論文架構 4
第二章 文獻探討 5
2.1 Big Data 5
2.2 雲端運算 8
2.2.1 Hadoop 11
2.2.2 MapReduce 14
2.3 推薦系統 16
2.3.1 記憶導向式協同過濾推薦(Memory-based Collaborative Filtering) 21
2.3.2 模型導向式協同過濾推薦(Model-based Collaborative Filtering) 24
2.3.2.1 基礎矩陣分解模型(Basic Matrix Factorization Model) 25
2.3.2.2 基準預測模型(Baseline Matrix Factorization Model) 26
2.3.3 推薦系統相關研究 27
第三章 研究方法 29
3.1 研究流程 29
3.2 模組功能介紹 31
3.2.1 驗證模組 31
3.2.2 註冊模組 32
3.2.3 預測模組 33
3.2.3.1 Content-Based Approach 33
3.2.3.2 基於MapReduce運算 36
3.2.4 推薦模組 51
3.3 案例模擬 52
第四章 實驗設計與結果 55
4.1 實驗環境 55
4.2 資料來源 55
4.3 評估指標 56
4.4 實驗情境與設計 56
4.4.1 情境說明 56
4.4.2 實驗設計 57
4.4.3 實驗相關參數設定 60
4.5 實驗分析與結果 61
第五章 結論 77
5.1 研究結論與貢獻 77
5.2 研究限制與建議 79
參考文獻 80
論文參考文獻:1. 胡世忠,雲端時代的殺手級應用,台灣:Big Data海量資料分析:天下雜誌,2013。
2. Adomavicius, G., & Tuzhilin, E. “Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering,” vol. 17, no. 6, 2005, pp. 734-749.
3. Breese, J. S., Heckerman, D., & Kadie, C., “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998, pp. 43-51.
4. Borthakur, D., “The Hadoop Distributed File System: Architecture and Design, Apache Software Foundation,” 2007, pp. 3−14.
5. Chen, L. C., Kuo, P. J., Liao, I. E., & Huang, J. Y., “Scaling out recommender system for digital libraries with MapReduce, Lecture Notes in Computer Science,” vol. 7861, 2013, pp. 40-47.
6. Connor, O. M., & Herlocker, J., “Clustering Items for Collaborative Filtering,” Proceedings of the Workshop on Recommender System: Algorithms and Evaluation, 1999.
7. Dean, J., & Ghemawat, S., “MapReduce: simplified data processing on large clusters, Communications of the ACM - 50th anniversary issue: 1958 - 2008,” vol. 51, no. 1, 2008, pp. 107-113.
8. Dooms, S., Audenaert, P., Fostier, J., Pessemier, T. D., & Martens, L. “In-memory, distributed content-based recommender system, Journal of Intelligent Information Systems,” 2013, pp. 1-25.
9. Dwivedi, P., & Bharadwaj, K. K., “e-Learning recommender system for a group of learners based on the unified learner profile approach, Expert Systems,” 2013.
10. Gartner Group, “The Importance of 'Big Data': A Definition,” 2012.
11. Goldberg, D., Nichols. D., Oki, B. M., & Terry, D., “Using Collaborative Filtering to Weave an Information Tapestry, Communications of the ACM - Special issue on information filtering,” vol. 35, no. 12, 1992, pp. 61-70.
12. Hu, R., & Pu, P., “A Comparative User Study on Rating vs. Personality Quiz based Preference Elicitation Methods,” Proceedings of the 14th international conference on Intelligent user interfaces, 2009, pp. 367-372.
13. Huang, X. F., Luo, X., & Zhu, Q. S., “A parallelization improvement on the Regularized Matrix Factorization based collaborative filtering, Journal of Electronics and Information Technology,” vol. 35, no. 6, 2013, pp. 1507-1511.
14. Huy, N., & Tien, D., “A Modified Regularized Non-Negative Matrix Factorization for MovieLens,” 2012 IEEE RIVF International Conference on Computing Communication Technologies, Research, Innovation, and Vision for the Future, Ho Chi Minh City, Vietnam, 2012, pp. 1-5.
15. Khanzadeh, Z., & Mahdavi, M., “Solving cold start problem in collaborative filtering method of recommender systems,” Proceedings of the ACM Symposium on Applied Computing, Sierre, Switzerland, 2010, pp. 96-100.
16. Koren, Y., Bell, R. M., & Volinsky, C., “Matrix factorization techniques for recommender systems, IEEE Computer Society,” vol. 42, no. 8, 2009, pp. 30-37.
17. Li, L., Li, C., Chen, H., & Du, X., “MapReduce-based simrank computation and its application in social recommender system,” IEEE International Congress on Big Data, BigData Congress 2013, California, USA, 2013, pp. 133-140.
18. Liang, T. P., Lai, H. J., & Ku, Y. C., “Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings, Journal of Management Information Systems,” vol. 23, no. 3, 2006, pp. 45-70.
19. Lika, B., Kolomvatsos, K., & Hadjiefthymiades, S., “Facing the cold start problem in recommender systems, Expert Systems with Applications” vol.41 no. 4, 2014, pp. 2065-2073.
20. Mack, S. J., “Human immunology in the era of big data, Human Immunology,” vol. 75, no. 1, 2014, pp. 2-3.
21. Mell, P., & Grance, T., “The NIST Definition of Cloud Computing: Recommendations of the National Institute of Standards and Technology, Special Publication 800-145,” 2011.
22. Montaner, M., Lopez, B., & de la Rosa, J. L., “A Taxonomy of Recommender Agents on the Internet, Artificial Intelligence Review,” vol. 19, no. 4, 2003, pp. 285-330.
23. Pazzani, M., “A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review - Special issue on data mining on the Internet,” vol. 13, no. 5, 1999, pp. 393-408.
24. Peng, J. X., & Liu, Z. Y., “The research and application of mapreduce based neighbor model in personalized recommendation, Applied Mechanics and Materials,” vol. 373-375, 2013, pp. 1674-1677.
25. Picciano, A. G., “The evolution of big data and learning analytics in american higher education, Journal of Asynchronous Learning Network,” vol. 16, no. 3, 2012, pp. 9-20.
26. Sachan, A., & Richhariya, V., “Reduction of Data Sparsity in Collaborative Filtering based on Fuzzy Inference Rules, International Journal of Advanced Computer Research,” vol. 3, no. 10, 2013, pp. 101-107.
27. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J., “Item-based collaborative filtering recommendation algorithms,” Proceedings of the 10th international conference on World Wide Web, Hong Kong, 2001, pp. 285-295.
28. Schelter, S., Boden, C., & Markl, V., “Scalable similarity-based neighborhood methods with mapreduce,” RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems, Dublin, Ireland, 2012, pp. 163-170.
29. Schlieski, T. & Johnson, B.D., “Entertainment in the age of big data,” Proceedings of the IEEE, vol. 100, 2012, pp. 1404-1408.
30. Tagiabadi, E. K., & Jalali, M., “Improve recommender systems using certain formulas and fuzzy concept networks,” IKT 2013 - 2013 5th Conference on Information and Knowledge Technology, Shiraz, Iran, 2013, pp. 236-241.
31. Kambatla, K., Pathak, A., & Pucha, H., “Towards Optimizing Hadoop Provisioning in the Cloud,” HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing, San Diego, California, 2009.
32. Tsai, C. S., & Chen, M. Y., “Using adaptive resonance theory and data-mining techniques for materials recommendation based on the e-library environment, Electronic Library,” vol. 26, no. 3, 2008, pp. 287-302.
33. Verbert, K., Lindstaedt, S. N., & Gillet, D., “Context-aware Recommender Systems J. UCS Special Issue, Journal of Universal Computer Science,” vol. 16, no. 16, 2010, pp. 2175-2178.
34. Vinod, B., “Leveraging BIG DATA for competitive advantage in travel, Journal of Revenue and Pricing Management,” vol. 12, no. 1, 2013, pp. 96-100.
35. Xu, W., “Research on traffic management-oriented "Big data" and its application,” 2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization, Shijiazhuang, China, vol. 427-429, 2013, pp. 2743-2747.
36. Zhang, L. F., Yang, S. W., & Zhang, M. W., “E-commerce website recommender system based on dissimilarity and association rule, TELKOMNIKA Indonesian Journal of Electrical Engineering,” vol. 12, no. 1, 2014, pp. 353-360.
37. Zhang, M., Wang, W., & Li, X., “A Paper Recommender for Scientific Literatures Based on Semantic Concept Similarity, Digital Libraries: Universal and Ubiquitous Access to Information,” vol. 5362, 2008, pp. 359-362.
38. Zhang, S., Li, C., Ma, L., & Li, Q., “Alleviating the sparsity problem of collaborative filtering using rough set, COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering,” vol. 32, no. 2, 2013, pp. 516-530.
39. Zhang, Y., Liu, H., & Li, S., “A distributed collaborative filtering recommendation mechanism for mobile commerce based on cloud computing, Journal of Information and Computational Science,” vol. 8, no. 16, 2011, pp. 3883-3891.
40. Zhu, Z., & Wang, J. Y., “Book Recommendation Service by Improved Association Rule Mining Algorithm,” Machine Learning and Cybernetics, 2007 International Conference on, Hong Kong, vol. 7, 2007, pp. 3864-3869.
論文全文使用權限:同意授權於2019-08-07起公開