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題名：結合學習向量量化與協同過濾之交換混合式過濾電影推薦架構
書刊名：資訊管理學報
作者：黃純敏 引用關係／林重佑／黃進瑞
作者(外文)：Huang, Chuen-min／Lin, Chung-yu／Huang, Jin-ruei
出版日期：2013
卷期：20:4
頁次：頁423-447
主題關鍵詞：學習向量量化；推薦系統；混合過濾；協同過濾；內容過濾；Learning vector quantization；Recommendation system；Hybrid filtering；Collaborative filtering；Content-based filtering
原始連結：連回原系統網址new window
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內容過濾與協同過濾是經常用於提供個人化服務的技術，近年來則多偏向結 合各種監督式學習的混合式過濾方式，並以三層或多層式網路架構產生推薦結 果，然而其設計不易且有網路收斂效率低的問題。本研究以學習向量量化（Learning Vector Quantization; LVQ）簡約的兩層式網路架構，運用交換（Switching）混合過 濾策略產生推薦內容。研究以MovieLens 資料集驗證方法架構。實驗發現，學習 向量量化可快速學習使用者多變的喜好。搭配交換混合過濾策略，可產生適切的 個人化內容，滿足不同使用者的推薦需求。研究結果顯示，本架構確可改善內容 過濾與協同過濾各自的缺點，整體精確率為79%，召回率為82%。
Content-based filtering and collaborative filtering are often used to provide personalized services technology. Recently, lots of supervised neural networks are combined with hybrid recommendation and adopted three layers or multiple layers to construct recommendation. Their drawbacks are slow convergence and hard to design. In this paper, we presented a novel switching hybrid recommendation framework based on two-layer Learning Vector Quantization (LVQ) to provide personalized recommendations. MovieLens data set was used to test our framework and the experiment indicated LVQ can quickly detect and learn from user preferences. Results showed that switching hybrid strategy provides promising personalized recommendation and satisfied the needs of different users. Our experiment gains 79% of precision, and the recall rate also reaches 82%.
期刊論文
1.Acilar, A. M.、Arslan, A.(2009)。A Collaborative Filtering Method Based on Artificial Immune Network。Expert Systems with Applications，36(4)，8324-8332。  new window
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3.Barragans-Martmez, A. B.、Costa-Montenegro, E.、Burguillo, J. C.、Rey-Lopez, M .、Mikic-Fonte, F. A.、Peleteiro, A.(2010)。A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition。Inform ation Sciences，180(22)，4290-4311。  new window
4.Callander, S.(2011)。Searching and learning by trial and error。The American Economic Review，101(6)，2277-2308。  new window
5.Chou, P. H.、Li, P. H.、Chen, K. K.、Wu, M. J.(2010)。Integrating web mining and neural network for personalized e-commerce automatic service。Expert Systems with Applications，37(4)，2898-2910。  new window
6.Chuan, Z.、Xianliang, L.、Mengshu, H.、Xu, Z.(2005)。A LVQ-based neural network anti-spam email approach。SIGOPS Operating Systems Review，39(1)，34-39。  new window
7.de Campos, L. M.、Femandez-Luna, J. M.、Huete, J. F.、Rueda-Morales, M. A.(2010)。Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks。International Journal of Approximate Reasoning，51(7)，785-799。  new window
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12.Martin-Valdivia, M.T.、Garcia-Vega, M.、Urena-Lopez, L. A.(2003)。LVQ for text categorization using a multilingual linguistic resource。Neurocomputing，55(3/4)，665-679。  new window
13.Martin-Valdivia, M. T.、Urena-Lopez, L. A.、Garcia-Vega, M.(2007)。The learning vector quantization algorithm applied to automatic text classification tasks。Neural Networks，20(6)，748-756。  new window
14.Putnam, H.(1965)。Trial and error predicates and the solution to a problem of Mostowski。The Journal of Symbolic Logic，30(1)，49-57。  new window
15.Sung, H. H.(2006)。Digital content recommender on the Internet。IEEE In telligent Systems，21(2)，70-77。  new window
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17.Symeonidis, P.、Nanopoulos, A.、Manolopoulos, Y.(2008)。Providing justifications in recommender systems。IEEE Transactions on Systems, Man and Cybernetics,Part A : Systems and Humans，38(6)，1262-1272。  new window
18.Umer, M.、Khiyal, M.(2007)。Classification of textual documents using learning vector quantization。Information Technology Journal，6(1)，154-159。  new window
19.Linde, Y.、Buzo, A.、Gray, R.(1980)。An algorithm for vector quantizer design。IEEE Transactions on Communications，28(1)，84-95。  new window
20.Balabanovic, M.、Shoham, Y.(1997)。Fab: content-based, collaborative recommendation。Communications of the ACM，40(3)，66-72。  new window
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23.Lekakos, G.、Caravelas, P.(2008)。A hybrid approach for movie recommendation。Multimedia Tools and Applications，36(1/2)，55-70。  new window
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會議論文
1.Breese, J. S.、Heckerman, D.、Kadie, C.(1998)。Empirical analysis o f predictive algorithms for collaborative filtering。the Fourteenth conference on Uncertainty in artificial intelligence，(會議日期: 1998/07/24-26)。Madison, Wisconsin。43-52。  new window
2.Christakou, C.、Stafylopatis, A.(2005)。A hybrid movie recommender system based on neural networks。the Fifth International Conference on In te llig e n t Systems Design and Applications，(會議日期: 2005/09/08-10)。Wroclaw, Poland。500-505。  new window
3.Das, A. S.、Datar, M.、Garg, A.、Rajaram, S.(2007)。Google news personalization:scalable online collaborative filtering。the Sixteenth international conference on World Wide Web，(會議日期: 2007/05/08-12)。Banff, Alberta, Canada。271-280。  new window
4.Kamahara, J.、Asakawa, T.、Shimojo, S.、Miyahara, H.(2005)。A community-based recommendation system to reveal unexpected interests。the Eleventh International Multimedia M odelling Conference，(會議日期: 2005/01/12-14)。Melbourne, Australia。433-438。  new window
5.Lang, K.(1995)。News Weeder: learning to filter netoews。the 12th International Conference on Machine Learning，(會議日期: 1995/07/09-12)。Lake Tahoe, CA。311-339。  new window
6.Li, Q.、Kim, B. M.(2003)。An approach for combining content-based and collaborative filters。the sixth international workshop on Information retrieval with Asian languages，(會議日期: 2003/07/07)。Sapporo, Japan。17-24。  new window
7.Marovic, M.、Mihokovic, M.、Miksa, M.、Pribil, S.、Tus, A.(2011)。Automatic movie ratings prediction using machine learning。the 34th International Convention on Information and Communication Technology, Electronics and Microelectronics，(會議日期: 2011/05/23-27)。Opatija, Croatia。1640-1645。  new window
8.Mizuno, Y.、Mabuchi, H.、Chakraborty, G.、Matsuhara, M.(2010)。Clustering of EEG data using maximum entropy method and LVQ。the 10th WSEAS international conference on Systems theory and scientific computation，(會議日期: 2010/08/20-22)。Taipei, Taiwan。  new window
9.Mooney, R. J.、Roy, L.(2000)。Content-based book recommending using learning for text categorization。the fifth ACM conference on Digital libraries，(會議日期: 2000/06/02-07)。San Antonio, Texas。195-204。  new window
10.Resnick, P.、Iacovou, N.、Suchak, M.、Bergstrom, P.、Riedl, J.(1994)。GroupLens:an open architecture for collaborative filtering of netnews。the 1994 ACM conference on Computer Supported Cooperative Work，(會議日期: 1994/10/22-26)。North Carolina：Chapel Hill。175-186。  new window
11.Yong, D.、Zhonghai, W.、Cong, T.、Huayou, S.、Hu, X.、Zhong，C.(2010)。A hybrid movie recommender based on ontology and neural networks。the IEEE/ACM International Conference on Green Computing and Communications, and International Conference on Cyber, Physical and Social Computing，(會議日期: 2010/12/18-20)。Hangzhou, China。846-851。  new window
12.Yu, P. S.(1999)。Data mining and personalization technologies。the Sixth International Conference on Database Systems fo r Advanced Applications，(會議日期: 1999/04/19-21)。Hsinchu, Taiwan。6-13。  new window
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圖書
1.Kohonen, T.、Hynninen, J.、Kangas, J.、Laaksonen, J.、Torkkola, K.(1996)。LVQPAK: the Learning Vector Quantization Program Package。FINLAND：Helsinki University of Technology。  new window
2.葉怡成(2003)。類神經網路模式--應用與實作。台北：儒林圖書公司。  延伸查詢new window
3.Kohonen, T.(1986)。Learning Vector Quantization for Pattern Recognition。Helsinki University of Technology, Department of Technical Physics, Laboratory of Computer and Information Science。  new window
其他
1.(2011)。Movie Lens Data Sets - Group Lens Research，http://www.grouplens.org/node/73， 2012/02/23。  new window
圖書論文
1.Burke, R.(2007)。Hybrid web recommender systems。The Adaptive Web: Methods and Strategies of Web Personalization。Springer-Verlag。  new window
 
 
 
 
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