在人們的日常生活中，推薦(Recommendation)已是很普遍的一種社會行為，而隨著科技的快速發展，資訊超載(Information Overload)使得個人在資訊的使用與搜尋上面臨極大的挑戰，更加刺激了對於推薦資訊的需求，因此許多推薦技術相繼被提出，推薦系統也相應而生，不僅使得推薦的範圍擴大了，推薦的型態也更為豐富多元；同時，在近年電子商務的發展中，對於個人化與顧客導向的熱烈推崇，使得推薦系統逐漸變成一種必要的線上服務。在眾多被提出的推薦技術之中，協同過濾推薦方法(collaborative filtering recommendation approach)是最成功且最常被採用的推薦技術。然而，傳統的協同過濾推薦方法忽略了推薦項目(Item)的內容相關性(content proximities)，在尋找與目標使用者品味相似者的過程中，將使用者對於每個項目的偏好都視為同等重要，並未去區分項目在內容上的親疏遠近，因此我們提出一個群集式的協同過濾推薦方法(cluster-based collaborative filtering recommendation approach)，將項目之間在內容上的相關性反應到協同過濾推薦方法中尋找品味相似者的過程。根據實證評估的結果，以群集的方式來改良傳統協同過濾推薦方法的確可以提高偏好預測時的準確度，而且不會犧牲預測的涵蓋範圍(coverage)。此外，由於資料的稀疏性(sparsity)問題，當可用的品味相似者(available neighbors)過少時，採用群內平均法(cluster average method)可以達到比所提方法更佳的預測準確度，因此我們考量了可用品味相似者數的影響，在最後提出一個改進的群集式協同過濾推薦方法(enhanced cluster-based collaborative filtering recommendation approach)，實證結果顯示，改進過的方法可以得到更佳的推薦效果。

Recommendation is not a new phenomenon arising from the digital era, but an existing social behavior in real life. Recommendation systems facilitate such natural social recommendation behavior and alleviate information overload facing individuals. Among different recommendation techniques proposed in the literature, the collaborative filtering approach is the most successful and widely adopted recommendation technique to date. However, the traditional collaborative filtering recommendation approach ignores proximities between items. That is, all user ratings on items are deemed identically important and given an equal weight in neighborhood formation process. In this study, we proposed a cluster-based collaborative filtering recommendation approach that takes into account the content similarities of items in the collaborative filtering process. Our empirical evaluation results show that the cluster-based collaborative filtering approach improves the prediction accuracy without sacrificing the prediction coverage, using those achieved by the traditional collaborative filtering approach as performance benchmarks. Due to the sparsity problem, when a prediction is made based on few neighbors, the cluster average method could achieve a better prediction accuracy than the proposed approach. Thus, we further proposed an enhanced cluster-based collaborative filtering approach that combines our approach and the cluster average method. The empirical results suggest that the enhanced approach could result in a prediction accuracy comparable to or even better than that accomplished by the cluster average method.

CHAPTER 1 INTRODUCTION 1
1.1 RESEARCH BACKGROUND 1
1.2 RESEARCH MOTIVATION AND OBJECTIVE 4
1.3 ORGANIZATION OF THE THESIS 5
CHAPTER 2 LITERATURE REVIEW 7
2.1 COLLABORATIVE FILTERING RECOMMENDATION APPROACH 7
2.1.1 Framework of Collaborative Filtering Recommendation Approach 8
2.1.2 Neighborhood Formation 9
2.1.3 Recommendation Generation 12
2.1.4 Strengths and Limitations of Collaborative Filtering Approach 13
2.2 DATA AND DOCUMENT CLUSTERING 14
2.2.1 Data Clustering 14
2.2.2 Document Clustering 17
CHAPTER 3 CLUSTER-BASED COLLABORATIVE FILTERING RECOMMENDATION 20
3.1 UNDERLYING RECOMMENDATION FRAMEWORK AND OVERALL PROCESS 20
3.2 ITEM-CLUSTERING 23
3.3 NEIGHBORHOOD FORMATION 25
3.3.1 Computation of User Similarity 25
3.3.2 Neighborhood Selection 27
3.4 RECOMMENDATION GENERATION 28
CHAPTER 4 EMPIRICAL EVALUATION 29
4.1 EVALUATION DATASETS 29
4.1.1 Movie Dataset 29
4.1.2 Literature Dataset 32
4.2 EVALUATION PROCEDURE AND METRICS 34
4.3 EVALUATION RESULTS 35
4.3.1 Cluster Formation of the Cluster-based Collaborative Filtering Approach 36
4.3.2 Effect of Neighbor Size 38
4.3.3 Comparative Evaluation with Collaborative Filtering Approach 43
4.3.4 Effect of Significance Weighting 45
4.3.5 Effect of Sparsity Level 50
4.3.6 Comparative Evaluation with Cluster Average Approach 53
CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS 58
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