臺灣博碩士論文加值系統

Recommender systems have become an important research area in past few years. They have been developed for a variety of domains, especially e-commerce. Recommender systems also can be applied in tourism industry to help tourists organizing their travel plans. Recommender systems can be developed by a variety of different techniques such as Content-Based filtering (CB), Collaborative Filtering (CF), and Demographic Filtering (DF). However, each method has its own advantages and disadvantages. For this reason, many previous researches used several mixed methods with an aim to reduce the disadvantages of using a single method and get more accurate recommendations.
In this research, we proposed a hybrid recommender system that combines the results of different recommendation methods using data mining techniques. Data mining technique is a method to dig out hidden knowledge and rules among the various items from large number of information and establish the relationship between model data attributes and categories in order to get more effective relationship model predictions.
The experimental results showed that the proposed hybrid recommendation method outperforms each individual recommendation method in terms of five evaluation metrics.

ABSTRACT iii
ACKNOWLEDGEMENT iv
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER ONE INTRODUCTION 1
1.1 Research Background and Motivation 1
1.2 Research Objectives 4
1.3 Research Scope 5
1.4 Research Process 5
1.5 Research Framework 6
CHAPTER TWO LITERATURE REVIEW 7
2.1 Recommender Systems 7
2.2 Recommender Systems Techniques 8
2.2.1 Content-Based filtering 8
2.2.2 Collaborative filtering 8
2.2.3 Demographic filtering 9
2.3 Data Mining Techniques 10
2.3.1 Linear regression 11
2.3.2 Artificial Neural Networks (ANN) 12
2.4 Hybrid Recommender Systems 14
2.4.1 Monolithic hybridization design 16
2.4.2 Parallel hybridization design 17
2.4.3 Pipelined hybridization design 18
2.5 Applications of Hybrid Recommender Systems 19
CHAPTER THREE RESEARCH METHOD 22
3.1 Introduction 22
3.2 System Architecture 22
3.2.1 Data Sources 23
3.2.2 Recommendation Techniques 23
3.1.1 Data Mining Techniques 29
3.2 System Assessment 30
3.2.1 Root-Mean-Square Error (RMSE) 31
3.2.2 Precision 31
3.2.3 Recall 32
3.2.4 F-Measure 32
3.2.5 Accuracy 32
CHAPTER FOUR EXPERIMENTAL RESULT & EVALUATION 33
4.1 Statistical Analysis of Experimental Data 33
4.2 Experimental Design 42
4.2.1 Content-Based Filtering 42
4.2.2 Collaborative Filtering 44
4.2.3 Demographic Filtering 45
4.2.4 Linear Regression Model 46
4.2.5 Neural Network Model 47
4.3 Analysis of the experimental results and evaluation 49
4.3.1 Root-mean-square error comparison of different models 50
4.3.2 Precision comparison of different models 51
4.3.3 Recall comparison of different models 53
4.3.4 F-measure comparison of different models 55
4.3.5 Accuracy comparison of different models 56
CHAPTER FIVE CONCLUSION 58
5.1 Conclusion 58
5.2 Future Work 59
REFERENCES 60
APPENDIXES 64

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