由於網際網路的發達，使得資訊的取得越來越容易，然而過多的資訊使得人們無法處理，我們將這種情形稱之為資訊過載。資訊擷取與資訊過濾即是為了因應資訊過載而發展出來的技術。推薦系統即是屬於資訊過濾的技術，推薦系統適用時使用者需求不明確無法以關鍵字代表時。
文獻上，協同過濾是推薦系統中最常用的技術，協同過濾是利用與目標對象興趣相仿的使用者意見來做出推薦。然而協同過濾往往會遭遇到資料過於稀疏的問題。資料稀疏指的是使用者的評比涵蓋率過低。在做出推薦的過程中，若資料過少時，計算使用者相似度時容易產生偏差，使得使用者相似度不夠穩定也不夠可靠。 為了因應資料稀疏的問題，協同過濾發展出了幾種變形。其中之一是以物品相似度取代傳統的使用者相似度，由於相似的物品容易被許多使用者所共同評比，因此在資料稀疏的狀態下，物品相似度會比使用者相似度可靠。而另外一種變形，則是以信任為基礎的協同過濾，以信任為基礎的協同過濾是在推薦的過程中，除了使用者相似度之外，加入了第二個觀點，使用者的信任值，來提升推薦的品質。
本研究的目的即是結合兩種協同過濾的變形(以物品為基礎以及以信任為基礎)，提出以物品層級的信任為基礎之協同過濾方法(ITBCF)，期望達到較佳的推薦品質，並且舒緩資料過於稀疏所產生的問題。我們使用了三個實驗，比較原始以信任為基礎之協同過濾方法(TBCF)以及ITBCF的推薦績效，發現在各種情況下ITBCF的有較佳的表現，這證明了我們在計算信任值時，考慮了物品的鄰居，能夠使得信任值更加可靠，並且提升推薦的品質。

With the rapid growth of Internet, more and more information is disseminated in the World Wide Web. It is therefore not an easy task to acquire desired information from the Web environment due to the information overload problem. To overcome this difficulty, two major methods, information retrieval and information filtering, arise. Recommender systems that employ information filtering techniques also emerge when the users’ requirements are too vague in mind to express explicitly as keywords.
Collaborative filtering (CF) refers to compare novel information with common interests shared by a group of people for recommendation purpose. But CF has major problem: sparsity. This problem refers to the situation that the coverage of ratings appears very sparse. With few data available, the user similarity employed in CF becomes unstable and thus unreliable in the recommendation process. Recently, several collaborative filtering variations arise to tackle the sparsity problem. One of them refers to the item-based CF as opposed to the traditional user-based CF. This approach focuses on the correlations of items based on users’ co-rating. Another popular variation is the trust-based CF. In such an approach, a second component, trust, is taken into account and employed in the recommendation process.
The objective of this research is thus to propose a hybrid approach that takes both advantages into account for better performance. We propose the item-level trust-based collaborative filtering (ITBCF) approach to alleviate the sparsity problem. We observe that ITBCF outperforms TBCF in every situation we consider. It therefore confirms our conjecture that the item-level trusts that consider neighbors can stabilize derived trust values, and thus improve the performance.

CHAPTER1 INTRODUCTION--1
1.1 OVERVIEW--1
1.2 OBJECTIVE OF THE RESEARCH--2
1.3 ORGANIZATION OF THE RESEARCH--3
CHAPTER 2 LITERATURE REVIEW--5
2.1 RECOMMENDER SYSTEMS--5
Content-based filtering--6
Collaborative filtering--6
Economic filtering--8
2.2 COLLABORATIVE FILTERING--8
Scalability--10
Sparsity--10
Cold start--11
2.3 COLLABORATIVE FILTERING TECHNIQUES--11
User-based collaborative filtering--11
Item-based collaborative filtering--12
Trust-based collaborative Filtering--15
CHAPTER 3 PROPOSED APPROACH--19
STEP 1: CALCULATING ITEM-ITEM SIMILARITY--20
STEP 2: INCORPORATING ITEM SIMILARITY INTO ITEM-LEVEL TRUST COMPUTATION--21
STEP 3: MAKING PREDICTED RECOMMENDATIONS--21
Filtering--22
Weighting--22
Combination--23
CHAPTER 4 EXPERIMENTS AND RESULTS--24
4.1 EXPERIMENTAL DESIGN--24
Data Collection--24
Objectives of the Experiments--25
Performance Measures--26
Evaluation Scheme--26
Parameter Settings--27
4.2 EXPERIMENTⅠ--27
4.2 EXPERIMENT II--29
4.3 EXPERIMENT III--36
CHAPTER 5 CONCLUSIONS--38
5.1 CONCLUDING REMARKS--38
5.2 FUTURE WORK--39
REFERENCE--40

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