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題名 學術研究論文推薦系統之研究
Development of a Recommendation System for Academic Research Papers
作者 葉博凱
貢獻者 梁定澎
葉博凱
關鍵詞 學術論文推薦
協同過濾
關聯規則
冷啟動
FP-Growth
recommendation systems
collaborative filtering
association rules
cold start
FP-Growth
日期 2014
上傳時間 3-Feb-2015 10:18:12 (UTC+8)
摘要 推薦系統為網站提升使用者滿意度、減少使用者所花費的時間並且替網站提供方提升銷售，是現在網站中不可或缺的要素，而推薦系統的研究集中在娛樂項目，學術研究論文推薦系統的研究有限。若能給予有價值的相關文獻，提供協助，無疑是加速進步的速度。
在過去的研究中，為了達到個人化目的所使用的方法，都有不可避免或未解決的缺點，2002年美國研究圖書館協會提出布達佩斯開放獲取計劃(Budapest Open Access Initiative)，不要求使用者註冊帳號與支付款項就能取得研究論文全文，這樣的做法使期刊走向開放的風氣開始盛行，時至今日，開放獲取對學術期刊網站帶來重大的影響。在這樣的時空背景之下，本研究提出一個適用於學術論文之推薦機制，以FP-Growth演算法與協同過濾做為推薦方法的基礎，消弭過去研究之缺點，並具個人化推薦的優點，經實驗驗證後，證實本研究所提出的推薦架構具有良好的成效。
Recommendation system is used in many field like movie, music, electric commerce and library. It’s not only save customers’ time but also raise organizations’ efficient. Recommended system is an essential element in a website. Some methods have been developed for recommended system, but they are primarily focused on content or collaboration-based mechanisms. For academic research, it is very important that relevant literature can be provided to researchers when they conduct literature review. Previous research indicates that there are inevitable or unsolved shortcomings in existing methods such as cold starts.
Association of Research Libraries purpose “Budapest Open Access Initiative” that is advocate open access concept. Open access means that users can get full paper without register and pay fee. It’s a major impact to academic journal website.
In this space-time background, we propose a hybrid recommendation mechanism that takes into consideration the nature of recommendation academic papers to mitigate the shortcomings of existing methods.
參考文獻 一、 中文部分
1. 余明哲，2002，圖書館個人化館藏推薦系統，國立交通大學碩士論文。
2. 邱建豪，2008，使用分群結合技術增進線上產品的推薦–以MovieLens為例，國立中正大學碩士論文。
3. 張景堯，2007，以多重觀點本體論驅策之系統發展方法，國立政治大學博士論文。
4. 許正怡，2008，植基於個人本體論模型與合作式過濾技術之中文圖書館推薦系統，國立中興大學碩士論文。
5. 郭秉仁，2012，基於個人本體論與MapReduce技術之圖書推薦系統，國立中興大學碩士論文。
6. 陳慧玲，2007，植基於個人本體論的圖書館推薦系統－以中興大學圖書館為例，國立中興大學碩士論文。
7. 廖學毅，2007，動態協同式過濾推薦之系統實做，國立交通大學碩士論文。
8. 蔡松霖，2013，電子商務推薦系統模型之初探，國立東華大學博士論文。
9. 羅子文，2007，Web 2.0概念的圖書館個人化推薦系統，國立交通大學碩士論文。
10. 楊永芳，2002，語意擴充式文件推薦方法之研究，國立中山大學碩士論文。
二、 英文部分
1. Adomavicius, G., & Tuzhilin, A. (2004). Recommendation Technologies: Survey of Current Methods and Possible Extensions (Working Paper). Stern School of Business, New York University.
2. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, pp. 207–216.
3. Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499.
4. Arslan, A., & Yilmazel, O. (2011). Frequent Pattern Mining Over Movie Plot Keywords. In International Conference on Computer and Computer Intelligence (ICCCI 2011), ASME Press.
5. Bobadilla, J., Ortega, F., Hernando, A., & Bernal, J. (2012). A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems, Vol. 26, 225–238.
6. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, Vol. 46, pp. 109–132.
7. Borgelt, C. (2005). An Implementation of the FP-growth Algorithm. Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, New York, NY, USA, pp. 1–5.
8. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, USA: Morgan Kaufmann Publishers Inc, pp. 43–52.
9. Christakou, C., & Stafylopatis, A. (2005). A hybrid movie recommender system based on neural networks. 5th International Conference on Intelligent Systems Design and Applications, 2005. ISDA ’05. Proceedings, pp. 500–505.
10. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM, Vol. 35(12), pp. 61–70.
11. Han, J., Pei, J., & Yin, Y. (2000). Mining Frequent Patterns Without Candidate Generation. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, pp. 1–12.
12. He, J., & Chu, W. W. (2010). A Social Network-Based Recommender System (SNRS). Data Mining for Social Network Data, pp. 47–74.
13. He, Q., Pei, J., Kifer, D., Mitra, P., & Giles, L. (2010). Context-aware Citation Recommendation. Proceedings of the 19th International Conference on World Wide Web, New York, NY, USA, pp. 421–430
14. Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An Algorithmic Framework for Performing Collaborative Filtering. Proceedings of the 22Nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, pp. 230–237.
15. Jinha, A. E. (2010). Article 50 million: an estimate of the number of scholarly articles in existence. Learned Publishing, Vol. 23(3), pp. 258–263.
16. Kantor, P. B., Rokach, L., Ricci, F., & Shapira, B. (2011). Recommender systems handbook. Springer.
17. Kim, B.-D., & Kim, S.-O. (2001). A new recommender system to combine content-based and collaborative filtering systems. Journal of Database Marketing & Customer Strategy Management, Vol. 8(3), pp. 244–252.
18. Kim, W., Choi, D. W., & Park, S. (2008). Agent based intelligent search framework for product information using ontology mapping. Journal of Intelligent Information Systems, Vol. 30(3), pp. 227–247.
19. Koren, Y. (2008). Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 426–434.
20. Lee, J., Lee, K., & Kim, J. G. (2013). Personalized Academic Research Paper Recommendation System. arXiv preprint arXiv:1304.5457.
21. Liang, T.-P., Yang, Y.-F., Chen, D.-N., & Ku, Y.-C. (2008). A semantic-expansion approach to personalized knowledge recommendation. Decision Support Systems, Vol. 45(3), pp. 401–412.
22. Lilien, G. L., Rangaswamy, A., Van Bruggen, G. H., & Starke, K. (2004). DSS Effectiveness in Marketing Resource Allocation Decisions: Reality vs. Perception. Information Systems Research, Vol. 15(3), pp. 216–235.
23. Lin, C.-W., Hong, T.-P., & Lu, W.-H. (2009). The Pre-FUFP algorithm for incremental mining. Expert Systems with Applications, Vol. 36(5), pp. 9498–9505.
24. Linden, G., Smith, B., & York, J. (2003). Amazon.Com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, Vol. 7(1), pp. 76–80.
25. LUO, J., & LI, Y. M. (2010). Improvement on Algorithm FP-Growth and Applications in Its E-Commerce. Journal of China West Normal University (Natural Sciences), 3, 018.
26. Matsatsinis, N. F., Lakiotaki, K., & Delias, P. (2007). A System based on Multiple Criteria Analysis for Scientific Paper Recommendation, Technical University of Crete.
27. McLaughlin, M. R., & Herlocker, J. L. (2004). A Collaborative Filtering Algorithm and Evaluation Metric That Accurately Model the User Experience. Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, pp. 329–336.
28. Naak, A., Hage, H., & Aïmeur, E. (2009). A Multi-criteria Collaborative Filtering Approach for Research Paper Recommendation in Papyres, E-Technologies: Innovation in an Open World, Springer Berlin Heidelberg, pp. 25–39.
29. Nunamaker, J. F., Jr., Chen, M., & Purdin, T. D. M. (1990). Systems Development in Information Systems Research. J. Manage. Inf. Syst., Vol. 7(3), pp. 89–106.
30. Palopoli, L., Rosaci, D., & Sarné, G. M. L. (2013). A Multi-tiered Recommender System Architecture for Supporting E-Commerce, Intelligent Distributed Computing VI . Springer Berlin Heidelberg, pp. 71–81.
31. Piateski, G., & Frawley, W. (1991). Knowledge discovery in databases. MIT press.
32. PIATETSKY-SHAPIRO, G. (1991). Discovery, Analysis and Presentation of Strong Rules. Knowledge Discovery in Databases, pp. 229–238.
33. Resnick, P., & Varian, H. R. (1997). Recommender Systems, Commun. ACM, Vol. 40(3), pp. 56–58.
34. Salton, G., Wong, A., & Yang, C. S. (1975). A Vector Space Model for Automatic Indexing. Commun. ACM, Vol. 18(11), pp. 613–620.
35. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of Recommendation Algorithms for e-Commerce. Proceedings of the 2Nd ACM Conference on Electronic Commerce, New York, NY, USA, pp. 158–167.
36. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th International Conference on World Wide Web ,New York, NY, USA, pp. 285–295
37. Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B., & Riedl, J. (1998). Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System. Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work, New York, NY, USA, pp. 345–354.
38. Sinha, R., Sinha, and R., & Swearingen, K. (2001). Comparing Recommendations Made by Online Systems and Friends. Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries.
39. Sugiyama, K., & Kan, M.-Y. (2010). Scholarly Paper Recommendation via User’s Recent Research Interests. Proceedings of the 10th Annual Joint Conference on Digital Libraries, New York, NY, USA, pp. 29–38.
40. Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Boston: Pearson Addison Wesley.
41. Wang, H.-F., & Wu, C.-T. (2012). A strategy-oriented operation module for recommender systems in E-commerce. Computers & Operations Research, Vol. 39(8), pp. 1837–1849.
42. Wang, K., Tang, L., Han, J., & Liu, J. (2002). Top Down FP-Growth for Association Rule Mining. , Advances in Knowledge Discovery and Data Mining. pp. 334–340.
43. Wang, Y., Liu, J., Dong, X., Liu, T., & Huang, Y. (2012). Personalized Paper Recommendation Based on User Historical Behavior. In M. Zhou, G. Zhou, D. Zhao, Q. Liu, & L. Zou (Eds.), Natural Language Processing and Chinese Computing, Springer Berlin Heidelberg. Retrieved from, pp. 1–12.
44. Xiaoyun, C., Yanshan, H., Pengfei, C., Shengfa, M., Weiguo, S., & Min, Y. (2009). HPFP-Miner: A Novel Parallel Frequent Itemset Mining Algorithm. In Fifth International Conference on Natural Computation, 2009. ICNC ’09, Vol.3, pp. 139–143.
45. Zaki, M. J. (2000). Scalable Algorithms for Association Mining. IEEE Trans. on Knowl. and Data Eng., Vol. 12(3), pp. 372–390.
 
三、 網路部分
1. Open Access, Association of Research Libraries, http://www.arl.org/focus-areas/open-scholarship/open-access。
 
描述 碩士
國立政治大學
資訊管理研究所
101356003
103
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101356003
資料類型 thesis
dc.contributor.advisor 梁定澎zh_TW
dc.contributor.author (Authors) 葉博凱zh_TW
dc.creator (作者) 葉博凱zh_TW
dc.date (日期) 2014en_US
dc.date.accessioned 3-Feb-2015 10:18:12 (UTC+8)-
dc.date.available 3-Feb-2015 10:18:12 (UTC+8)-
dc.date.issued (上傳時間) 3-Feb-2015 10:18:12 (UTC+8)-
dc.identifier (Other Identifiers) G0101356003en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/73238-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 101356003zh_TW
dc.description (描述) 103zh_TW
dc.description.abstract (摘要) 推薦系統為網站提升使用者滿意度、減少使用者所花費的時間並且替網站提供方提升銷售，是現在網站中不可或缺的要素，而推薦系統的研究集中在娛樂項目，學術研究論文推薦系統的研究有限。若能給予有價值的相關文獻，提供協助，無疑是加速進步的速度。
在過去的研究中，為了達到個人化目的所使用的方法，都有不可避免或未解決的缺點，2002年美國研究圖書館協會提出布達佩斯開放獲取計劃(Budapest Open Access Initiative)，不要求使用者註冊帳號與支付款項就能取得研究論文全文，這樣的做法使期刊走向開放的風氣開始盛行，時至今日，開放獲取對學術期刊網站帶來重大的影響。在這樣的時空背景之下，本研究提出一個適用於學術論文之推薦機制，以FP-Growth演算法與協同過濾做為推薦方法的基礎，消弭過去研究之缺點，並具個人化推薦的優點，經實驗驗證後，證實本研究所提出的推薦架構具有良好的成效。		zh_TW
dc.description.abstract (摘要) Recommendation system is used in many field like movie, music, electric commerce and library. It’s not only save customers’ time but also raise organizations’ efficient. Recommended system is an essential element in a website. Some methods have been developed for recommended system, but they are primarily focused on content or collaboration-based mechanisms. For academic research, it is very important that relevant literature can be provided to researchers when they conduct literature review. Previous research indicates that there are inevitable or unsolved shortcomings in existing methods such as cold starts.
Association of Research Libraries purpose “Budapest Open Access Initiative” that is advocate open access concept. Open access means that users can get full paper without register and pay fee. It’s a major impact to academic journal website.
In this space-time background, we propose a hybrid recommendation mechanism that takes into consideration the nature of recommendation academic papers to mitigate the shortcomings of existing methods.		en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第二章 文獻探討 5
第一節 推薦系統定義與概述 5
第二節 相關研究 5
第三節 推薦系統分類 13
第四節 推薦系統常見問題 16
2.4.1 推薦系統的共同問題 16
2.4.2 協同推薦系統的常見問題 16
2.4.3 內容基礎推薦的常見問題 17
第五節 關聯規則探勘 18
2.5.1 關聯規則之定義與目的 18
2.5.2 關聯規則探勘方法 19
第六節 小結 20
第三章 研究方法 21
第一節 設計科學的研究方法 21
第二節 期刊網站現況 22
第三節 推薦系統設計 24
第四節 資料蒐集 27
3.4.1 計算閱讀習慣相似之輸入與評分 27
第五節 詞頻分析模組 28
第六節 使用者相似度計算模組 29
3.6.1. Pearson變數說明與公式 30
第七節 關聯規則分析模組 30
3.7.1 FP-tree之建構與表示 30
3.7.2 FP-growth高頻項目集之產生 32
第四章 實驗設計 36
第一節 研究假說 37
第二節 資料前置處理 38
第三節 實驗設計與實驗流程 38
第四節 實證結果分析 41
4.4.1 分析方法 41
4.4.2 資料分析 41
第五章 結論 43
第一節 研究結果 43
第二節 研究貢獻 44
第三節 研究限制 45
第四節 未來研究方向 45		zh_TW
dc.format.extent 1687443 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101356003en_US
dc.subject (關鍵詞) 學術論文推薦zh_TW
dc.subject (關鍵詞) 協同過濾zh_TW
dc.subject (關鍵詞) 關聯規則zh_TW
dc.subject (關鍵詞) 冷啟動zh_TW
dc.subject (關鍵詞) FP-Growthzh_TW
dc.subject (關鍵詞) recommendation systemsen_US
dc.subject (關鍵詞) collaborative filteringen_US
dc.subject (關鍵詞) association rulesen_US
dc.subject (關鍵詞) cold starten_US
dc.subject (關鍵詞) FP-Growthen_US
dc.title (題名) 學術研究論文推薦系統之研究zh_TW
dc.title (題名) Development of a Recommendation System for Academic Research Papersen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 一、 中文部分
1. 余明哲，2002，圖書館個人化館藏推薦系統，國立交通大學碩士論文。
2. 邱建豪，2008，使用分群結合技術增進線上產品的推薦–以MovieLens為例，國立中正大學碩士論文。
3. 張景堯，2007，以多重觀點本體論驅策之系統發展方法，國立政治大學博士論文。
4. 許正怡，2008，植基於個人本體論模型與合作式過濾技術之中文圖書館推薦系統，國立中興大學碩士論文。
5. 郭秉仁，2012，基於個人本體論與MapReduce技術之圖書推薦系統，國立中興大學碩士論文。
6. 陳慧玲，2007，植基於個人本體論的圖書館推薦系統－以中興大學圖書館為例，國立中興大學碩士論文。
7. 廖學毅，2007，動態協同式過濾推薦之系統實做，國立交通大學碩士論文。
8. 蔡松霖，2013，電子商務推薦系統模型之初探，國立東華大學博士論文。
9. 羅子文，2007，Web 2.0概念的圖書館個人化推薦系統，國立交通大學碩士論文。
10. 楊永芳，2002，語意擴充式文件推薦方法之研究，國立中山大學碩士論文。
二、 英文部分
1. Adomavicius, G., & Tuzhilin, A. (2004). Recommendation Technologies: Survey of Current Methods and Possible Extensions (Working Paper). Stern School of Business, New York University.
2. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, pp. 207–216.
3. Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499.
4. Arslan, A., & Yilmazel, O. (2011). Frequent Pattern Mining Over Movie Plot Keywords. In International Conference on Computer and Computer Intelligence (ICCCI 2011), ASME Press.
5. Bobadilla, J., Ortega, F., Hernando, A., & Bernal, J. (2012). A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems, Vol. 26, 225–238.
6. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, Vol. 46, pp. 109–132.
7. Borgelt, C. (2005). An Implementation of the FP-growth Algorithm. Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, New York, NY, USA, pp. 1–5.
8. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, USA: Morgan Kaufmann Publishers Inc, pp. 43–52.
9. Christakou, C., & Stafylopatis, A. (2005). A hybrid movie recommender system based on neural networks. 5th International Conference on Intelligent Systems Design and Applications, 2005. ISDA ’05. Proceedings, pp. 500–505.
10. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM, Vol. 35(12), pp. 61–70.
11. Han, J., Pei, J., & Yin, Y. (2000). Mining Frequent Patterns Without Candidate Generation. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, pp. 1–12.
12. He, J., & Chu, W. W. (2010). A Social Network-Based Recommender System (SNRS). Data Mining for Social Network Data, pp. 47–74.
13. He, Q., Pei, J., Kifer, D., Mitra, P., & Giles, L. (2010). Context-aware Citation Recommendation. Proceedings of the 19th International Conference on World Wide Web, New York, NY, USA, pp. 421–430
14. Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An Algorithmic Framework for Performing Collaborative Filtering. Proceedings of the 22Nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, pp. 230–237.
15. Jinha, A. E. (2010). Article 50 million: an estimate of the number of scholarly articles in existence. Learned Publishing, Vol. 23(3), pp. 258–263.
16. Kantor, P. B., Rokach, L., Ricci, F., & Shapira, B. (2011). Recommender systems handbook. Springer.
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三、 網路部分
1. Open Access, Association of Research Libraries, http://www.arl.org/focus-areas/open-scholarship/open-access。
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