進階搜尋


下載電子全文  
系統識別號 U0026-1001201209285700
論文名稱(中文) 資料前處理及影片搜尋與推薦之資料探勘技術
論文名稱(英文) Data Pre-Processing and Data Mining Techniques for Video Retrieval and Recommendation
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 100
學期 1
出版年 101
研究生(中文) 王博文
研究生(英文) Bo-Wen Wang
學號 P7892110
學位類別 博士
語文別 英文
論文頁數 76頁
口試委員 指導教授-曾新穆
口試委員-謝孫源
口試委員-高宏宇
口試委員-林文揚
口試委員-李建億
口試委員-林威成
中文關鍵字 資料探勘  資料前處理  遺失值處理  協同式資訊過濾技術  影片擷取  影像註解  影片推薦 
英文關鍵字 Data mining  data pre-processing  missing-value imputation  collaborative filtering  video retrieval  image annotation  video recommendation 
學科別分類
中文摘要 近年來,資料探勘技術受到高度之研究矚目並蓬勃發展,各種資料探勘技術已被廣泛地應用到許多領域,如:資訊檢索、生物醫學資訊探勘、行動模式探勘及多媒體資料探勘等。本研究主要在探討適用於資料前處理及影片搜尋與推薦之資料探勘技術。首先,在資料探勘的研究中,為了確保得到高品質的探勘結果,資料前處理是極為重要的。因此,就有很多方法被提出來要解決在基因表現微陣列資料中有關遺失值的問題。本研究提出了一個名為CFBRST (Collaborative Filtering Based on Rough-Set Theory)的演算法,並將其應用於基因表現微陣列資料之遺失值處理中。由於協同式資訊過濾技術(Collaborative Filtering)有良好的效果,所以經常被應用於推薦系統。CFBRST就是根據協同式資訊過濾技術和模糊理論(Rough-Set Theory)所設計出來的方法。經由真實資料之驗證實驗,顯示出CFBRST演算法可以有效地改善遺失值的問題,特別是在遺失值比較多的情況之下,CFBRST演算法的改善情況就會更加明顯。
由於影片擷取設備的普及化及YouTube影片分享服務的出現,使得影片搜尋成為一個很重要的研究主題。目前已經有許多的影片搜尋方法被提出,但只有少數是聚焦在以影像來搜尋影片的方法,這是由於影像和影片之間會存在著語意差距的緣故。本研究提出一個使用語意化的影片搜尋系統,這個系統結合了影像註解及概念比對函數將影像和影片根據它們的概念來做比對,使得可以利用影像來做影片的搜尋。針對網頁的影像註解,包括有文字資訊和視覺化的資訊可以應用。而概念比對函數則是應用WordNet來計算影像概念和影片概念的相似度,來找出影像和影片之間的關係。實驗結果也顯示出這個系統可以將使用者的意念由影像概念轉換成影片概念。
另外,相對於被動式的影片搜尋技術,本研究發展一個二階段式的影片推薦技術,可以主動提供給使用者有興趣的影片。一般而言,推薦系統主要是可以籍由使用者的行為模式,利用機器學習的方法來處理資訊過量的問題。雖然傳統的協同式資訊過濾技術可以有效地預測使用者的喜好樣式,但還是有資料稀疏性的問題要去克服。為了要降低資料稀疏性的影響,本研究提出一種創新的協同式資訊過濾技術的推薦方法,這個推薦方法將預測的程序分解成二個階段。在第一個階段,會將使用者沒有評分過的影片來做預估值的填補,然後再利用這些填補值在第二階段做預測的計算。實驗的結果顯示本方法有很好的效率,尤其是當資料庫的資料為非常稀疏的情況之下,本方法之表現顯著優於傳統協同式資訊過濾技術的方法。
英文摘要 In recent years, data mining techniques have been widely used in many fields, such as information retrieval, biomedical mining, mobile mining, and multimedia data mining. This thesis investigates the data mining techniques for data pre-processing, as well as video retrieval and recommendation techniques. First, data pre-processing greatly affects the quality of mining results. One of the issues in data pre-processing is the handling of missing values, where the goal is to recover their original values. In this dissertation, an intelligent imputation approach named Collaborative Filtering based on Rough-Set Theory (CFBRST) is proposed to impute missing values with applications on microarray gene expression datasets. The collaborative filtering (CF) approach is often used in recommender systems due to its excellent performance. The proposed CFBRST method is based on the CF method and rough-set theory. Experimental results on real microarray gene expression datasets reveal that the proposed approach can effectively improve missing-value estimation. The results show that the CFBRST method produces more accurate results than k-nearest neighbors (k-NN) approach for yeast cDNA microarray datasets, especially when the percentage of missing values is high.
Video retrieval has become an increasingly important topic due to the prevalence of video recording devices and the advent of media-sharing services such as YouTube. Numerous video retrieval approaches have been proposed, but few studies have focused on querying videos with images due to the semantic gap between them. This dissertation proposes a semantic video retrieval system that integrates web image annotation and a concept matching function to bridge images, concepts, and videos. For web image annotation, the textual and visual information in an image are exploited. For the concept matching function, concept relations are identified by calculating the similarity between image concepts and video concepts using WordNet, a large English lexical database. The experimental results reveal that the proposed system can transform user intens from image concepts to video concepts.
In contrast to passive video retrieval, in this dissertation, a two-phase recommender system is also proposed to actively provide users with their preferred videos. Although the traditional CF method has been shown to be effective in predicting a user’s preferences for recommendation, it suffers from the data sparsity problem. To alleviate this problem, an innovative CF recommender that decomposes the prediction procedure into two phases is proposed. In the first phase, the user’s unknown ratings are imputed as the initial ratings to provide information for the second prediction phase. Experimental evaluation results show the effectiveness of the proposed method, especially for situations with very sparse data.
論文目次 中文摘要 IV
ABSTRACT VI
誌 謝 VIII
Content IX
List of Figures XI
List of Tables XIII
Chapter 1 Introduction 1
1.1 Motivation 2
1.2 Overview of the Dissertation 3
1.2.1 Framework of Data Pre-Processing 4
1.2.2 Framework of Image-Based Video Retrieval 5
1.2.3 Framework of Two-Phase Video Recommender System 6
1.3 Dissertation Organization 6
Chapter 2 Background and Related Work 7
2.1 Missing-Value Imputation 7
2.2 Collaborative Filtering algorithms 8
2.3 Image Annotation 10
2.4 Video Retrieval 11
2.5 Video Recommendation 12
Chapter 3 Improving Missing-Value Estimation in Microarray Data with Collaborative Filtering Based on Rough-Set Theory 14
3.1 Introduction 14
3.2 Proposed Method 16
3.2.1 Overview of the proposed approach 17
3.2.2 Preprocessing stage 18
3.2.3 Prediction stage 20
3.3 Experimental Evaluation 26
3.3.1 Datasets 26
3.3.2 Evaluation Metrics 27
3.3.3 Evaluations for the parameter settings 28
3.3.4 Comparisons between CFBRST and other approaches 30
3.4 Summary 32
Chapter 4 Semantic Video Retrieval by Integrating Concept- and Content-Aware Mining 33
4.1 Introduction 33
4.2 Proposed System 35
4.2.1 System Framework 35
4.2.2 Off-line Training Phase 37
4.2.3 On-line Query Phase 39
4.3 Experimental Evaluations 41
4.3.1 Experimental Results for Image Annotation 42
4.3.2 Experimental Results for Concept Matching 44
4.4 Summary 45
Chapter 5 An Effective Two-Phase Collaborative Filtering Algorithm for Recommender Systems 46
5.1 Introduction 46
5.2 Proposed System 47
5.2.1 Overview of the proposed approach 47
5.2.2 Off-line preprocessing phase 49
5.2.3 On-line prediction stage 51
5.3. Empirical evaluations 55
5.3.1 Impact of parameters on the performance of the proposed approach 57
5.3.2 Comparison of two-phase approaches and traditional approaches 58
5.3.3 Comparison of the impact of data sparsity on the performances of the two-phase approach and item-based CF 59
5.3.4 Empirical Study 60
5.4 Summary 60
Chapter 6 Conclusion and Future Work 62
6.1 Conclusion 62
6.2 Future Work 63
References 65
Publications 75

參考文獻 [1]E. Acuna and C. Rodriguez, “The Treatment of Missing Values and Its Effect in the Classifier Accuracy,” Classification, Clustering and Data Mining Applications, pp.639–648, 2004.
[2]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, no.6, pp.634–749, 2005.
[3]M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,” Communications of the ACM, vol.40, no.3, pp.66-72, 1997.
[4]J. Basilico, and T. Hofmann, “Unifying Collaborative and Content-Based Filtering,” Proc. ACM International Conference on Machine Learning, pp.65-72, 2004.
[5]C. Basu, H. Hirsh and W. Cohen, “Recommendation as Classification: Using Social and Content-Based Information in Recommendation,” Proc. of the 15th National Conference on Artificial Intelligence, pp.714-720, 1998.
[6]R. M. Bell, Y. Koren and C. Volinsky, “Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems,” Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.
[7]Y. Blanco-Fernandez, J. J. Pazos-Arias, M. Lopez-Nores, A. Gil-Solla and M. Ramos-Cabrer, “VATAAR: An Improved Solution for Personalized TV Based on Semantic Inference,” IEEE Transaction on Consumer Electronics, vol.52, pp.421-429, 2006.
[8]D. Bollegala, Y. Matsuo and M. Ishizuka, “Measuring Semantic Similarity between Words Using Web Search Engine,” Proc. of the 16th international conference on World Wide Web, pp.757-766, 2007.
[9]J. S. Breese, D. Heckerman and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, pp.43-52, 1998.
[10]X. Cai, M. Bain, A. Krzywicki, W. Wobcke, Y.S. Kim, P. Compton and A. Mahidadia, “Collaborative Filtering for People to People Recommendation in Social Networks,” AI 2010: Advances in Artificial Intelligence, 2010
[11]E. Camon, M. Magrane, D. Barrell, V. Lee, E. Dimmer, J. Maslen, D. Binns, N. Harte, R. Lopez and R. Apweiler, “The Gene Ontology Annotations (GOA) Database: Sharing Knowledge in Uniprot with Gene Ontology,” Nucleic Acids Research, vol.32, pp.262–266, 2004.
[12]E. Chang, K. Goh, G. Sychay and G. Wu, “CBSA: Content-Based Soft Annotation for Multimodal Image Retrieval Using Bayes Point Machines,” IEEE Transactions on Circuits and Systems for Video Technology, 2003.
[13]Z.-H. Che, “A Two-Phase Hybrid Approach to Supplier Selection through Cluster Analysis with Multiple Dimensions,” International Journal of Innovative Computing, Information and Control, vol.6, no.9, pp.4093-4111, 2010.
[14]L.-S. Chen, C.-C. Hsu and Y.-S. Chang, “Developing a Novel Two-Phase Learning Scheme for the Class Imbalance Problem,” International Journal of Innovative Computing, Information and Control, vol.6, no.11, pp.4979-4994, 2010
[15]P. J. Cheng and L. F. Chien, “Personalized Image Browsing and Annotation on the Web Using Query Taxonomy,” Proc. of International Conference on Digital Archive Technologies, 2002.
[16]W. G. Cheng and D. Xu, “Content-Based Video Retrieval Using the Shot Cluster Tree,” Proc. of the 2nd IEEE International Conference on Machine Learning and Cybernetics, pp.2901-2906, 2003.
[17]Y. H. Chien and E. I. George, “A Bayesian Model for Collaborative Filtering,” Proc. Of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.
[18]M. G. Christel, “Carnegie Mellon University Traditional Informedia Digital Video Retrieval System,” Proc. of the International Conference on Image and Video Retrieval, Amsterdam, The Netherlands, 2007.
[19]Y. Chuan, X. Jieping and D. Xiaoyong, “Recommendation Algorithm Combining the User-Based Classified Regression and the Item-Based Filtering,” Proc. of the 8th International Conference on Electronic Commerce, pp.574–578, 2006.
[20]M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, “Combining Content-Based and Collaborative Filters in an Online Newspaper,” Proc. of the ACM SIGIR Workshop Recommender Systems, 1999.
[21]M. Condliff, D. Lewis, D. Madigan, and C. Posse, “Bayesian Mixed-Effects Models for Recommender Systems,” Proc. of the ACM SIGIR Workshop Recommender Systems, 1999.
[22]M. Couto, M. Silva and P. Coutinho, “Measuring Semantic Similarity between Gene Ontology Terms,” Data & Knowledge Engineering, vol.61, pp.137-152, 2007.
[23]S. Debnath, N. Ganguly and P. Mitra, “Feature Weighting in Content Based Recommendation System Using Social Network Analysis,” Proc. of the 17th International Conference on World Wide Web, pp.1041-1042, 2008
[24]M. Degemmis, P. Lops, and G. Semeraro, “A Content-Collaborative Recommender that Exploits Wordnet-Based User Profiles for Neighborhood Formation,” User Modeling and User-Adapted Interaction, vol.17, no.3, pp.217-255, 2007
[25]J. Delgado and N. Ishii, “Memory-Based Weighted-Majority Prediction for Recommender Systems,” Proc. of the ACM SIGIR Workshop Recommender Systems: Algorithms and Evaluation, 1999.
[26]J. L. DeRisi, V. R. Iyer and P. O. Brown, “Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale,” Science, vol.278, pp.680-686, 1997.
[27]S. Dudoit, Y. H. Yang, M. J. Callow and T. P. “Speed, Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments,” Statistica Sinica, vol.12, pp.111-139, 2002.
[28]H. Feng, R. Shi and T. S. Chua, “A Bootstrapping Framework for Annotating and Retrieving WWW Images,” Proc. of annual ACM international conference on Multimedia, 2004.
[29]M. D. Flickner, H. Sawhney, W. Niblack, J.Ashley, Q.Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Steele and P. Yanker, “Query by Image and Video Content: The QBIC System,” Computer, 28(9):23-32, 1995.
[30]G. Gaughan, A. F. Smeaton, C. Gurrin, H. Lee, and K. Mc Donald, “Design, Implementation and Testing of an Interactive Video Retrieval System,” Proc. of the 5th ACM SIGMM international workshop on Multimedia information retrieval, pp.23-30, 2003.
[31]M. de Gemmis, P. Lops, G. Semeraro, and P. Basile, “Integrating Tags in a Semantic Content-Based Recommender,” Proc. of the 2nd ACM Conference on Recommender Systems, pp.163-170, 2008.
[32]T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H.Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. BloomField and E. S.Lander, “Molecular Classification for Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring,” Science, vol.286, pp.531-537, 1999.
[33]N. Good, J. B. Schafer, J. A. Konstan, A. Borchers, B. Sarwar, J. Herlocker and J. Riedl, “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” Proc. of the 16th National Conference on Artificial Intelligence, pp.439-446, 1999.
[34]S. W. Han and J. Y. Kim, “A New Decision Tree Algorithm Based on Rough Set Theory,” International Journal of Innovative Computing, Information and Control, vol.4, no.10, pp.2749-2757, 2008.
[35]A. Haubold and A. (Paul) Natsev, “Web-Based Information Content and its Application to Concept-Based Video Retrieval,” Proc. of international conference on Content-based image and video retrieval, pp.437-446, 2008.
[36]L. Herlocker and J. A. Konstan, “Content-Independent Task-Focused Recommendation,” IEEE Internet Computing, vol.5, no.6, pp.40-47, 2001.
[37]L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Transaction on. Information Systems, vol.22, no.1, pp.5-53, 2004.
[38]T. P. Hong, L. H. Tseng and S. L. Wang, “Learning Rules from Incomplete Training Examples by Rough Sets,” Expert Systems with Applications, vol.22, pp.285-293, 2002.
[39]X. Hu, V. Sanghvi, B. Vong, P. J. On, C. Leong, and J. Angelica, “Moody: A Web-Based Music Mood Classification and Recommendation System,” Proc. of the 9th International Conference on Music Information Retrieval, 2008
[40]J. Jeon, V. Lavrenko, and R. Manmatha, “Automatic Image Annotation and Retrieval Using Cross-Media Relevance Models,” Proc. of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, pp.119 – 126, 2003.
[41]X. Jin, Y. Zhou and B. Mobasher, “A Maximum Entropy Web Recommendation System: Combining Collaborative and Content Features,” Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2005.
[42]H. Kim, G. H. Golub and H. Park, “Missing Value Estimation for DNA Microarray Gene Expression Data: Local Least Squares Imputation,” Bioinformatics, vol.21, pp.187-198. 2005
[43]Y. T. Kim and T. S. Chua, “Retrieval of News Video using Video Sequence Matching,” Proc. of the 11th International Multimedia Modelling Conference, 2005.
[44]J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of the ACM, vol.40, no.3, pp.77-87, 1997.
[45]Y. Koren, “Collaborative Filtering with Temporal Dynamics,” Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.447-456, 2009.
[46]M. Kryszkiewiccz, “Rough Set Approach to Incomplete Information System,” Information Sciences, vol.112, pp.39 - 49, 1998.
[47]V. Lavrenko, S.L. Feng and R. Manmatha, “Statistical Models for Automatic Video Annotation and Retrieval,” Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.17-21, 2004.
[48]J. Liang and Z. Xu, “Uncertainty Measures of Roughness of Knowledge and Rough Sets in Incomplete Information Systems,” The Third World Congress on Intelligent Control and Automation, vol.4, pp.2526-2529, 2000.
[49]C. Lin, Z. Jing, J. Watada, T. Kashima, and H. Ishii, “A Rough Set Approach to Classification and its Application for the Creative City Development,” International Journal of Innovative Computing, Information and Control, vol.5, no.12(B), pp.4859-4866, 2009.
[50]F.-T. Lin and T.-R. Tsai, “A Two-Stage Genetic Algorithm for Solving the Transportation Problem with Fuzzy Demands and Fuzzy Supplies,” International Journal of Innovative Computing, Information and Control, vol.5, no.12(B), pp.4775–4785, December 2009.
[51]W. Lin, S. A. Alvarez and C. Ruiz, “Collaborative Recommendation via Adaptive Association Rule Mining,” Proc. of the International Workshop on Web Mining for E-Commerce, 2000.
[52]G. Linden, B. Smith and J. York, “Amazon.Com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, vol.7, no.1, pp.76-80, 2003.
[53]J. Liu, M. Li, W.Y. Ma, Q. Liu and H. Lu, “An Adaptive Graph Model for Automatic Image Annotation,” Proc. of ACM International Conference on Multimedia Information Retrieval, pp. 61-70, 2006.
[54]X. Liu, W. Wu and J. Hu, “A Method of Fuzzy Multiple Attribute Decision Making Based on Rough Sets,” International Journal of Innovative Computing, Information and Control, vol.4, no.8, pp.2005-2010, 2008.
[55]X. Liu, Y. Zhuang, and Y. Pan, “A New Approach to Retrieve Video by Example Video Clip,” Proc. of the 7th ACM international conference on Multimedia, pp.41-44, 1999.
[56]D. J. Lockhart, and E. A. Winzeler, “Genome, Gene Expression and DNA Arrays,” Nature, vol.405, pp.827-836, 2000.
[57]P. W. Lord, R. D. Stevens, A. Brass and C. A. Goble, “Investigating Semantic Similarity Measures Across the Gene Ontology: the Relationship between Sequence and Annotation,” Bioinformatics, vol.19, pp.1275-1283, 2003.
[58]H. Ma, H. Yang, M. R. Lyu, and I. King, “SoRec: Social Recommendation Using Probabilistic Matrix Factorization,” Proc. of CIKM, pp.931-940, 2008.
[59]M. R. McLaughlin and J. Herlocker, “A Collaborative Filtering Algorithm and Evaluation Metric that Accurately Model the User Experience,” Proc. of the SIGIR Conference on Research and Development in Information Retrieval, pp.329-336, 2004.
[60]T. Mei, Y. Wang, X.S. Hua, S. Gong and S. Li, “Coherent Image Annotation by Learning Semantic Distance,” Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
[61]P. Melville, R. J. Mooney and R. Nagarajan, “Content-Boosted Collaborative Filtering for Improved Recommendations,” Proc. of the 18th National Conference on Artificial Intelligence, pp.187-192, 2002.
[62]B. Mobasher, R. Burke, R. Bhaumik and C. Williams, “Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems,” Proc. of the 2005 WebKDD Workshop, 2005.
[63]M. S. Mohamad, S. Omatu, M. Yoshioka and S. Deris, “A Two-Stage Method to Select a Smaller Subset of Informative Genes for Cancer Classification,” International Journal of Innovative Computing, Information and Control, vol.5, no.10(A), pp.2959–2968, 2009.
[64]R. J. Mooney and L. Roy, “Content-Based Book Recommending Using Learning for Text Categorization,” Proc. of the 5th ACM Conference on Digital Libraries, pp.195-204, 2000.
[65]H. Q. Nguyen and Q. T. Ngo, “A Novel Content Based Image Retrieval Method Based on Splitting the Image into Homogeneous Regions,” International Journal of Innovative Computing, Information and Control , vol.6, no.9, pp.4029-4040, 2010.
[66]D. Nikovski, and V. Kulev, “Induction of Compact Decision Trees for Personalized Recommendation,” Proc. of the ACM symposium on Applied computing, pp.575-581, 2006.
[67]S. Oba, M. A. Sato and I. Takemasa, “A Bayesian Missing Value Estimation Method for Gene Expression Profile Data,” Bioinformatics, vol.19, pp.2088-2096, 2003.
[68]J. Y. Pan, H.J. Yang, C. Faloutsos, P. Duygulu, “Automatic Multimedia Cross-Modal Correlation Discovery,” Proc. of ACM SIGKDD international conference on Knowledge discovery and data mining, pp.22-25, 2004.
[69]T. Pedersen, S. Patwardhan and J. Michelizzi, “WordNet::Similarity - Measuring the Relatedness of Concepts,” Proc. of American Association for Artificial Intelligence, 2004. Avaliable at: http://marimba.d.umn.edu/cgi-bin/similarity/similarity.cgi.
[70]Y. Peng and C. W. Ngo, “Clip-Based Similarity Measure for Query-Dependent Clip Retrieval and Video Summarization,” IEEE Transactions on Circuits and Systems for Video Technology, vol.16, no.5, pp.612-627, 2006.
[71]A. Popescul, L. H. Ungar, D. M. Pennock and S. Lawrence, “Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments,” Proc. of the 17th Conference in Uncertainty in Artificial Intelligence, pp.437-444, 2001.
[72]M. Rautiainen, T. Ojala, and T. Seppänen, “Analyzing the Performance of Visual, Concept and Text Features in Content-Based Video Retrieval,” Proc. of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp.197-204, 2004.
[73]P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. of the ACM Conference on Computer Supported Cooperative Work, pp.175-186, 1994.
[74]S. Rho, B. J. Han, and E. Hwang, “Svr-Based Music Mood Classification and Context-Based Music Recommendation,” In MM’09: Proc. of the 17th ACM International Conference on Multimedia, pp.713-716, 2009.
[75]H. M. Sanderson and M. D. Dunlop, “Image Retrieval by Hypertext Links,” Proc. of ACM SIGIR, 1997.
[76]B. M. Sarwar, G. Karypis, J. A. Konstan and J. Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms,” Proc. of the 10th International World Wide Web Conference, pp.285-295, 2001.
[77]I. Schein, A. Popescul and L. H. Ungar, “Generative Models for Cold-Start Recommendations,” Proc. of the 2001 ACM SIGIR Workshop on Recommender Systems, 2001.
[78]M. S. Sehgal, I. Gondal and L. S. Dooley, “Collateral Missing Value Imputation: a New Robust Missing Value Estimation Algorithm for Microarray Data,” Bioinformatics, vol.21, pp.2417-2423, 2005.
[79]U. Shardanand and P. Maes, “Social Information Filtering: Algorithms for Automating “Word of Mouth” ,” Proc. of the SIGCHI conference on Human factors in computing systems, pp.210 - 217, 1995.
[80]H. T. Shen, B. C. Ooi and K. L. Tan, “Giving Meaning to WWW Images,” Proc. of the 8th annual ACM international conference on Multimedia, 2000.
[81]J. Shi, and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue 8, pp.888 – 905, 2000.
[82]J. R. Smith and S. F. Chang, “An Image and Video Search Engine for the World-Wide Web,” Proc. of IS&T/SPIE Symposium on Electronic Imaging: Science and Technology Storage and Retrieval for Image and Video Databases, 1997.
[83]J. H. Su, Y. T. Huang, H. H. Yeh and Vincent. S. Tseng, “Effective Content-Based Video Retrieval Using Pattern Indexing and Matching Techniques,” Expert Systems with Applications, vol. 37, issue 7, pp.5068-5085, 2010.
[84]X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, vol.2009, 2009.
[85]G. Takacs, I. Pilaszy, B. Nemeth and D. Tikk, “Scalable Collaborative Filtering Approaches for Large Recommender Systems,” Journal of Machine Learning Research, vol.10, pp.623-656, 2009.
[86]S. Tan, X. Cheng and H. Xu, “An Efficient Global Optimization Approach for Rough Set Based Dimensionality Reduction,” International Journal of Innovative Computing, Information and Control, vol.3, no.3, pp.725-736, 2007.
[87]O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein and R. B. Altman, “Missing Value Estimation Methods for DNA Microarray,” Bioinformatics, vol.17, pp.520–525, 2001.
[88]J. Tuikkala, L. Elo, O. S. Nevalainen, and T. Aittolallio, “Improving Missing Value Estimation in Microarray Data with Gene Ontology,” Bioinformatics, vol.21, no.5, pp.566-572, 2006.
[89]L. H. Ungar and D. P. Foster, “Clustering Methods for Collaborative Filtering,” Proc. of the Workshop on Recommender Systems at the 15th National Conference on Artificial Intelligence, pp.114-129, 1998.
[90]J. Wang, A. P. de Vries and M. J. T. Reinders, “Unifying User-Based and Item-Based Collaborative Filtering Approaches by Similarity Fusion,” Proc. of the 29th ACM SIGIR Conference on Information Retrieval, pp.501-508, 2006.
[91]J. Z. Wang, J. Li and G. Wiederhold, “SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, no.9, 2001.
[92]C. F. Wong and C. H. C. Leung, “Automatic Semantic Annotation of Real-World Web Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, no.11, pp.1933-1944, 2008.
[93]L. Wu, X. S. Hua, N. Yu, W. Y. Ma and S. Li, “Flickr Distance,” Proc. of ACM International conference on Multimedia, pp.31-40, 2008.
[94]L. Wu, M. Li, Z. Li, W. Y. Ma and N. Yu, “Visual Language Modeling for Image Classification,” Proc. of ACM SIGMM International Workshop on Multimedia Information Retrieval in conjunction with ACM Multimedia, pp.115-124, 2007.
[95]J. A. Xu and K. Araki, “A SVM-Based Personal Recommendation System for TV Programs,” Proc. of the 12th Multi-Media Modeling Conference, 2006.
[96]G. R. Xue, C. Lin, Q. Yang, W. Xi, H. J. Zeng, Y. Yu and Z. Chen, “Scalable Collaborative Filtering Using Cluster-Based Smoothing,” Proc. of the 28th ACM SIGIR Conference on Research and development in information retrieval, pp.114-121, 2005.
[97]K. Yoshii, M. Goto, K. Komatani, T. Ogata and H. G. Okuno, “Hybrid Collaborative and Content-Based Music Recommendation Using Probabilistic Model with Latent User Preferences,” Proc. of the International Conference on Music Information Retrieval (ISMIR), pp.296-301, 2006.
[98]K. Yu, A. Schwaighofer, V. Tresp, X. Xu and H. P. Kriegel, “Probabilistic Memory-Based Collaborative Filtering,” IEEE Transactions on Knowledge and Data Engineering, vol.16, no.1, pp.56-69, 2004.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2014-01-17起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2014-01-17起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw