伴隨網際網路的快速成長，使得資訊容易取得，但大量的資料出現也造成人們想要搜尋及獲取所需資料時，遇到資訊過載的問題。資訊擷取與資訊過濾的相關技術被發展來輔助我們的閱讀及理解能力。採用資訊過濾技術的推薦系統隨之興起，適用於使用者的需求不明確而無法以關鍵字表示的時候。
協同過濾技術常被用於推薦系統中，其技術為利用與目標使用者有相似興趣的其它使用者的意見來做出推薦。其中一種協同過濾技術是以模型為基礎，可以將使用者過去的意見建立學習模型，並利用建立的模型進行推薦預測。然而以模型為基礎之協同過濾要考慮二個問題：其一是資料的可靠度(是否含雜訊、冗贅的資料)會影響其預測結果；其二是目前大部分的模型視資料輸出為名義尺度，而忽略了評比資料是次序尺度。
因此本研究的目的是提出增進資料可靠度及考慮資料尺度的協同過濾模型，期望能得到較佳的推薦結果。我們提出三個實驗來驗證比較。實驗結果顯示，我們所提的方法有不錯的績效表現，特別是在稀疏度不太高或是大型資料集的時候。這些結果也因此驗證我們所提方法於實際應用的可行性。

Accompanying with the Internet growth explosion, more and more information disseminates on the Web. The large amount of information, however, causes the information overload problem that disturbs users who desire to search and find useful information online. Information retrieval and information filtering arise to compensate for the searching and comprehending ability of the users. Recommender systems as one of the information filtering techniques emerge when users cannot describe their requirements precisely as keywords.
Collaborative filtering (CF) compares novel information with common interests shared by a group of people to make the recommendations. One of its methods, the Model-based CF, generates predicted recommendation based on the model learned from the past opinions of the users. However, two issues on model-based CF should be addressed. First, data quality of the rating matrix input can affect the prediction performance. Second, most current models treat the data class as the nominal scale instead of ordinal nature in ratings.
The objective of this research is thus to propose a model-based CF algorithm that considers data reliability and data scale in the model. Three experiments are conducted accordingly, and the results show our proposed method outperforms other counterparts especially under data of mild sparsity degree and of large scale. These results justify the feasibility of our proposed method in real applications.

CHAPTER 1 Introduction 1
1.1 Overview 1
1.2 Objective of the research 3
1.3 Organization of the research 3
CHAPTER 2 Literature Review 4
2.1 Recommender systems 4
Content-based methods 4
Collaborative methods 5
Hybrid methods 7
2.2 Collaborative Filtering 7
2.3 Singular Vector Decomposition Analysis 10
2.3.1 SVD Factorization 11
2.3.2 SVD with generalized Hebbian learning rule 13
2.4 Support Vector Machine 14
CHAPTER 3 Proposed Approach 19
Step 1: Data processing with Hebbian learning SVD 20
Step 2: Building the SVOR_based model 21
Step 3: Predicting the ratings 24
CHAPTER 4 Experiments and Results 25
4.1 Experimental Design 25
Dataset Descriptions 25
Objectives of the Experiments 27
Performance Measures 27
Evaluation Scheme 27
Platform 28
4.1 Experiment I 28
4.2 Experiment II 31
4.3 Experiment III 34
CHAPTER 5 Conclusions 36
5.1 Concluding remarks 36
5.2 Future work 37
REFERENCE 39

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