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
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