Reference: | 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。 |