隨著網際網路的快速發展，網際網路現今已成為人們生活中不可或缺的一部份，給人們生活上帶來了許多便利性，人們也越來越依賴網際網路上的資訊，但隨著時間累積，網際網路上的資料量及物件也越來越龐大，人們面臨了資訊超載的問題，面對如此巨大的資料庫人們可能很快的在網際網路上看到自己有興趣的事物，雖然現在有著強大的搜尋引擎可供人們快速的找尋物件，但仍然存在著人們無法主動發掘感興趣的物件的問題，因此許多網路供應商或電子商務系統主動的提供推薦系統，使人們可以較有效率的找到自己可能感興趣物的物件，系統供應商使用較佳的推薦系統更是能從中得到利益。協同過濾(Collaborative Filtering)在推薦系統中較為廣泛使用，協同過濾藉由使用者的歷史行為(使用過的、評分過的物件)與系統中其他使用者的歷史行為做比對分析，找出相似行為的使用者來預測其對那些物件可能會感興趣，因較容易實作所以被廣泛的使用，而傳統的協同過濾方法主要是在系統中對目標使用者找出固定數量的相似使用者，來當作目標使用者的參考對象分析並預測目標使用者可能的行為，本研究藉由使用者相似度演算法並且使用分群方法將相似興趣取向的人們自動分配在同個群組，以同群組的使用者當作參考，並對同個群組的使用者作出行為的預測來達到推薦的目的。並使用Movielens電影評分資料庫的數據來驗證預測的結果。

With the rapid development of the Internet, the Internet has become an indispensable part of people's lives nowadays. In addition to the size of the user population, the size of website databases has also increased significantly in recent years. People are trying to explore interesting content in this huge web database. In such a rich website resources also bring people “information overload” problems. In order to solve this problem, recommender systems have been widely used in many applications as well as e-commerce or media web sites, such as Amazon.com, Netflix.com and Last.fm. The system can automatically recommend the items to users that fit their interests. So that users can quickly find the content or objects of interest to them.
In this thesis, we focus on collaborative filtering, which is one of the common types of recommender systems. Collaborative filtering approach is the most successful and most widely used in recommender system approach. In this study, we develop a new clustering-based collaborative filtering recommender system. By using the user similarity model and using the clustering method to automatically assign people of similar interest to the same group. Conduct predictions of behavior by users of the same group to achieve the recommended purpose.

[1] X. Su and T. M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, vol. 2009, 2009.
[2] J. O'Donovan and B. Smyth, "Trust in recommender systems," in Proceedings of the 10th international conference on intelligent user interfaces, Pages 167-174, 2005.
[3] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry," Communications of the ACM, vol. 35, no. 12, Pages 61–70, 1992.
[4] G. Linden, B. Smith, and J. York, "Amazon.com recommendations: item-to-item collaborative filtering," IEEE Internet Computing, vol. 7, no. 1, Pages. 76–80, 2003.
[5] MovieLens dataset, [Online].
Available: https://grouplens.org/datasets/movielens/
[6] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, "Evaluating collaborative filtering recommender systems," ACM Transactions on Information Systems, vol. 22, no. 1, Pages 5–53, 2004.
[7] Foreseeing Innovative New Digiservices (FIND), [Online]. Available: https://www.find.org.tw/market_info.aspx?n_ID=9023
[8] Netflix prize, [Online].
Available: http://www.netflixprize.com/.
[9] M. D. Ekstrand, J. T. Riedl and J. A. Konstan, "Collaborative Filtering Recommender Systems" Foundations and Trends in Human–Computer Interaction, vol. 4, no. 2, Pages. 81–173, 2010.
[10] Chris Biemann, "Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems" Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, Pages 73-80, 2006.
[11] H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, "A new user similarity model to improve the accuracy of collaborative filtering" Knowledge-Based Systems vol. 56, Pages 156–166, 2014.
[12] U. Shardanand, and P. Maes, "Social Information Filtering: Algorithms for Automating "Word of Mouth"", in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Pages 210–217 ,1994.
[13] G. Adomavicius, and A. Tuzhilin, "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, Pages 734-749, 2005.
[14] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "Grouplens: an open architecture for collaborative filtering of netnews," in Proceedings of the ACM Conference on Computer Supported Cooperative Work, Pages 175–186, 1994.
[15] F. Maxwell Harper and Joseph A. Konstan, "The MovieLens Datasets: History and Context." ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 5, Article 19 ,2015.
[16] F. Cacheda, V. Carneiro, D. Fernández, and V. Formoso, "Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender system." ACM Transactions on the Web, vol. 5, pp. 1-33, 2011.
[17] A. Ansari, S. Essegaier, and R. Kohli, "Internet recommendation systems," Journal of Marketing Research, vol. 37, Pages 363–375, 2000.
[18] W. Hill, L. Stead, M. Rosenstein, and G. Furnas, "Recommending and evaluating choices in a virtual community of use," in Proceedings of the ACM CHI 95 Human Factors in Computing Systems Conference, Pages 194–201, 1995.
[19] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," ACM WWW ’01, Pages 285–295, 2001.
[20] J. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," in Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI '98), Pages 43-52, 1998.
[21] Y. Koren, "Factorization meets the neighborhood: a multifaceted collaborative filtering model," in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '08), Pages 426–434, 2008.
[22] L. Ungar and D. Foster, "Clustering Methods for Collaborative Filtering," in Proceedings of Workshop on Recommendation Systems, AAAI Press, 1998.
[23] S. Gong, "A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering," Journal of Software, vol. 5, 2010.
[24] X. Liu, "An improved clustering-based collaborative filtering recommendation algorithm," Cluster Computing, vol. 20, Pages 1281–1288, 2017.