推薦系統是一個常見的資訊過濾系統，不管於商業或個人都是非常重要的技術，可以依據使用者不同的興趣給予不同的物件。本體(Ontology)能將相同領域內的同一套概念進行概念化表達。本研究藉由自動建立本體，讓眾多未分類的文件進行階層式的分群，並應用於使用者輪廓的建立，把使用者的興趣對應至本體進行階層式分群管理。另外，本研究改善使用者興趣於本體上的對應，並加入長期興趣的考量，使得使用者興趣的偵測更準確。在推薦方面，本研究利用本體中的結構相似度來找出使用者的隱性興趣，讓推薦的文件更多樣化。
本研究於實驗中採用亞馬遜網路商店的書籍簡介當作資料集，並模擬不同情況下，使用者閱讀文件而產生的興趣變化，由實驗結果得出本系統可以改善推薦準確度並能讓推薦的文件更多樣。期望本系統能用於商業層面，讓企業更準確得知顧客的興趣並帶來更大的利益。

Recommended system is a common information filtering system. It’s a very important technology no matter in business or person, and it can provide users different documents according to their different interests. We can use ontology to conceptualize the concepts within the same domain. This study automatically construct ontology to let the unclassified document hierarchical clustering and applied to create user profile. Users’ interests can be mapped to ontology in order to manage interests by hierarchical clustering. Besides, this study improve the method to mapping user’s interests to ontology and added long term interest into the method to make the detection of users’ interests more accurate. In the recommendation, this study use structure similarity at the ontology to find the implicit interests and let the recommendation of documents be more diverse.
In the experiments, this study use book’s description on Amazon online shopping websites as data collection and simulate the change of users’ interest in different conditions. We could find that this study can improve the accurate of recommendation and make recommendation more diverse. Expects this system to be used for commercial, let enterprises to more accurately know the customer's interest and bring more benefits.

[1] 李浩平. (2011). 運用NGD 建立適用於使用者回饋資訊不足之文件過濾系統. 國立中央大學資訊管理學系碩士論文.
[2] 李佩儒. (2014). 利用自建Ontological User Profile應用於文字文件推薦. 國立中央大學資訊管理學系碩士論文.
[3] 陳信夫. (2011). 基於字詞關係動態建立階層分群. 國立中央大學資訊管理學系碩士論文.
[4] 詹欣逸. (2012). 利用WordNet 判斷字詞包含關係─應用於動態階層文件分群. 國立中央大學資訊管理學系碩士論文
[5] 賴靜怡. (2013). 自動建立ontology 應用於user profile 建立. 國立中央大學資訊管理學系碩士論文.
[6] 鄭奕駿. (2012). 離線搜尋 Wikipedia 以縮減 NGD 運算時間之研究. 中央大學資訊管理學系碩士論文.
[7] Adomavicius, G., & Tuzhilin, A. (2005). 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, 17(6), 734-749.
[8] Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval (Vol. 463): ACM press New York.
[9] Chen, P.-I., & Lin, S.-J. (2010). Automatic keyword prediction using Google similarity distance. Expert Systems with Applications, 37(3), 1928-1938.
[10] Cilibrasi, R. L., & Vitanyi, P. (2007). The google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370-383.
[11] eMarketer. (2013). Ecommerce Sales Topped $1 Trillion for First Time in 2012.
[12] Giaretta, P., & Guarino, N. (1995). Ontologies and knowledge bases towards a terminological clarification. Towards very large knowledge bases: knowledge building & knowledge sharing, 25, 32.
[13] Gil-García, R., & Pons-Porrata, A. (2010). Dynamic hierarchical algorithms for document clustering. Pattern Recognition Letters, 31(6), 469-477.
[14] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70.
[15] Han, L., Chen, G., & Li, M. (2013). A method for the acquisition of ontology-based user profiles. Advances in Engineering Software, 65, 132-137.
[16] Hawalah, A., & Fasli, M. (2014). Utilizing contextual ontological user profiles for personalized recommendations. Expert Systems with Applications, 41(10), 4777-4797.
[17] Hawalah, A., & Fasli, M. (2015). Dynamic user profiles for web personalisation. Expert Systems with Applications, 42(5), 2547-2569.
[18] Li, Q., Wang, J., Chen, Y. P., & Lin, Z. (2010). User comments for news recommendation in forum-based social media. Information Sciences, 180(24), 4929-4939.
[19] McAuley, J., & Leskovec, J. (2013). Hidden factors and hidden topics: understanding rating dimensions with review text. Paper presented at the Proceedings of the 7th ACM conference on Recommender systems.
[20] Middleton, S. E., Shadbolt, N. R., & De Roure, D. C. (2004). Ontological user profiling in recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 54-88.
[21] Pérez-Suárez, A., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., & Medina-Pagola, J. E. (2013). An algorithm based on density and compactness for dynamic overlapping clustering. Pattern Recognition, 46(11), 3040-3055.
[22] Rada, R., Mili, H., Bicknell, E., & Blettner, M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 19(1), 17-30.
[23] Razmerita, L., & Lytras, M. D. (2008). Ontology-based user modelling personalization: Analyzing the requirements of a semantic learning portal Emerging Technologies and Information Systems for the Knowledge Society (pp. 354-363): Springer.
[24] Shiller, R. J. (1979). The volatility of long-term interest rates and expectations models of the term structure. The Journal of Political Economy, 1190-1219.
[25] Sieg, A., Mobasher, B., & Burke, R. D. (2007). Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search. IEEE Intelligent Informatics Bulletin, 8(1), 7-18.
[26] Sussna, M. (1993). Word sense disambiguation for free-text indexing using a massive semantic network. Paper presented at the Proceedings of the second international conference on Information and knowledge management.
[27] Tang, X., & Zeng, Q. (2012). Keyword clustering for user interest profiling refinement within paper recommender systems. Journal of Systems and Software, 85(1), 87-101.
[28] Weng, S.-S., Lin, B., & Chen, W.-T. (2009). Using contextual information and multidimensional approach for recommendation. Expert Systems with Applications, 36(2), 1268-1279.
[29] Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. Paper presented at the Proceedings of the 32nd annual meeting on Association for Computational Linguistics.