隨著網際網路的發展，使得資訊的取得變的相當容易，但是要從眾多的資訊中找出符合使用者需求的內容就相當困難了。目前除了藉由搜尋引擎以關鍵字的方式找出相關資料外，另一個方法則是藉由推薦系統幫助使用者獲得感興趣的資訊。推薦系統分析過去使用者喜好或興趣相近的使用者來篩選大量的資訊，以節省在網路中搜尋的時間。
目前較常被用於推薦的技術有以內容為主過濾和協同過濾，經由文獻和各方面資訊皆顯示協同過濾優於以內容為主過濾，主要原因為協同過濾不受限於內容和過去喜好的限制，但是其本身的主要問題為資料稀疏問題，使得推薦準確率下降。近年來許多學者發展出許\多方法來解決協同過濾之資料稀疏問題，其中包括物品式協同過濾和信任式協同過濾。本研究的目的是希望藉由實驗設計的方式來評估這兩種協同過濾演算法在解決資料稀疏問題上的績效，經由設定不同情況(譬如:資料密集度、資料大小、鄰居數目等)來分析比較何者較佳。
本研究提出兩個實驗來驗證比較。實驗的結果顯示，信任式協同過濾在緩解資料稀疏性上的效果是較佳的，但是其差異會隨稀疏程度減少而愈不明顯；同時，隨著資料量的上升，信任式協同過濾所需花費的時間成長卻會比物品式協同過濾還要多的多。最後，二種方法的最佳鄰居數並不隨資料量的大量成長而成長，反而僅小幅增加，維持一定的穩健度。

With the dramatic growth of the Internet, it is much easier for us to acquire information than before. It is, however, relatively difficult to extract desired information through the huge information pool. One method is to rely on the search engines by analyzing the queried keywords to locate the relevant information. The other one is to recommend users what they may be interested in via recommender systems that analyze the users’ past preferences or other users with similar interests to lessen our information processing loadings.
Typical recommendation techniques are classified into content-based filtering technique and collaborative filtering (CF) technique. Several research works in literature have indicated that the performance of collaborative filtering is superior to that of content-based filtering in that it is subject to neither the content format nor users’ past experiences. The collaborative filtering technique, however, has its own limitation of the sparsity problem. To relieve such a problem, researchers proposed several CF-typed variants, including item-based CF and trust-based CF. Few works in literature, however, focus on their performance comparison. The objective of this research is thus to evaluate both approaches under different settings such as the sparsity degrees, data scales, and number of neighbors to make recommendations.
We conducted two experiments to examine their performance. The results show that trust-based CF is generally better than item-based CF in sparsity problem. Their difference, however, becomes insignificant with the sparsity decreasing. In addition, the computational time for trust-based CF increases more quickly than that for item-based CF, even though both exhibit exponential growths. Finally, the optimal number of nearest neighbors in both approaches does not heavily depend on the data scale but displays steady robustness.

CHAPTER 1 Introduction 1
1.1 Overview 1
1.2 Objective of the research 2
1.3 Organization of the thesis 3
CHAPTER 2 Literature Review 4
2.1 Recommender systems 4
Content-based filtering 5
Collaborative Filtering 5
2.2 Collaborative filtering approaches 8
User-based collaborative filtering 8
Item-based collaborative filtering 9
2.3 Trust on the semantic web 10
2.4 Trust-based collaborative filtering 11
CHAPTER 3 Collaborative Filtering Approaches Under Study 14
3.1 Item-based Collaborative Filtering 14
Step 1: Compute item-item similarity 15
Step 2: Select K most similar items 16
Step 3: Make recommendations 17
3.2 Trust-based Collaborative Filtering 17
Step 1: Compute trust value of users 17
Step 2: Incorporate trust values into CF 19
Step 3: Make recommendations 20
CHAPTER 4 Experiments and Results 22
4.1 Experimental Design 22
4.2 Experiment I 24
4.3 Experiment II 31
CHAPTER 5 Conclusions 34
5.1 Concluding remarks 34
5.2 Future work 35
References 36

黃信傑，以協同過濾輔助內容分析之文件推薦系統, 中山大學資訊管理研究所碩士論文,民95
Deshpande, M and Karypis, G, Item-Based Top-N Recommendation Algorithms, ACM Trans, Information System, vol. 22, no. 1, pages 143-177, 2004.
Miller, E, Weaving Meaning: An Overview of The Semantic Web. Retrieved May 26, 2004, from the World Wide Web: http://www.w3.org/2004/Talks/0120-semweb-umich.
Golbeck, J and Hendler, J, Reputation Network Analysis for Email Filtering. Proceedings of the First Conference on Email and Anti-Spam, 2004.
Huang, Z, Chen, H and Zeng, D, Applying Associative Retrieval Techniques
to Alleviate the Sparsity Problem in Collaborative Filtering, ACM Transactions on Information Systems, Vol. 22, No. 1, January, Pages 116–142, 2004.
Golbeck J and Hendler, J. Accuracy of metrics for inferring trust and reputation in semantic web-based social networks. In Proceedings of EKAW’04, pages LNAI 2416, p. 278 ff., 2004.
Massa, P and Bhattacharjee, B. Using trust in recommender systems: an experimental analysis. Proceedings of 2nd International Conference on Trust Managment, Oxford, England, 2004.
Massa, P and Avesani, P. Trust-aware collaborative filtering for recommender systems. Proceedings of International Conference on Cooperative Information Systems, Agia Napa, Cyprus, 25 Oct – 29 Oct 2004.
Melville, P, Mooney, R.J., and Nagarajan, R, Content-boosted collaborative filtering for improved recommendations. Proceedings of the 18th National Conference on Artifical Intelligence, 2002.
Montaner, M, Lopez, B and Lluis, J de la Rosa. Developing trust in recommender agents. In Proceedings of the first international joint conference on Autonomous agents and multiagent systems, pages 304–305. ACM Press, 2002.
Mooney, R, J and Roy L, Content-based book recommending using learning for text categorization, Proceedings of the fifth ACM conference on Digital libraries, pages 195-204, 2000.
O’ Donovan, J., & Smyth, B. Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces, Pages: 167 – 174, 2005.
O’ Donovan, J., & Smyth, B. Is Trust Robust? An Analysis of Trust-Based Recommendation.
Proceedings of the 11th international conference on Intelligent user interfaces, 2006.
Papagelis, M, Plexousakis, D and Kutsuras T, Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences, Proceedings of the 3rd International Conference on Trust Management, pages 224-239, 2005.
Pitsilis, G. and Marshall, L, Trust as a key to improving Recommendation Systems, Published by the University of Newcastle upon Tyne, School of Computing Science, 2004.
Pitsilis, G. and Marshall, L, A Model of Trust Derivation from Evidence for Use in Recommendation, Published by the University of Newcastle upon Tyne, School of Computing Science, 2004.
Resnick, P, Iacovou, N, Suchak, M, Bergstrom, P, and Riedl, J. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM CSCW’94 Conference on Computer-Supported Cooperative Work, Sharing Information and Creating Meaning, pages 175–186, 1994.
Resnick, P, Recommender system, Association for Computing Machinery, Communications of the ACM, New York, vol 40, pages 56-58, 1997.
Sawar, B, Karypis, G, Konstan, J and Riedl, J, Application of Dimensionality Reduction in Recommender System -- A Case Study, ACM WebKDD 2000 Web Mining for E-Commerce Workshop, 2000.
Sawar, B, Karypis, G, Konstan, J and Riedl, J, Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th international conference on World Wide Web, pages 285-295, 2001.
Schafer, J. B, Konstan, J and Riedl, J, Electronic Commerce Recommender Applications, Journal of Data Mining and Knowledge Discovery, vol 5, pages 115-152, 2000.
Soboroff, I and Nicholas, C, Combining Content and Collaboration in Text Filtering, Proc. Int’l Joint Conf. Artificial Intelligence Workshop: Machine Learning for Information Filtering, Aug. 1999.
W3C(2001). Semantic Web. Retrieved May 30, 2004, from the World Wide Web: http://www.w3.org/2001/sw/
Weng, J, Miao, C, Goh, A, Improving collaborative filtering with trust-based metrics. Proceedings of the 2006 ACM symposium on Applied computing, Pages: 1860 – 1864, 2006.