隨著網際網路的興起與蓬勃發展，使用者能夠便利地從網際網路中搜尋物品，但是大量的物品資訊卻也使得使用者難以消化，使用推薦技術便是克服這個資訊超載問題的一項解決之道。推薦技術透過建議使用者可能喜歡或感興趣的商品來加快搜尋商品的速度，對企業而言，也能夠藉以促進更多的消費與電子商務網站的使用率。近年來由於Web 2.0技術的發展，以信任關係為基礎的推薦技術正逐漸興起，透過信任關係的分析，參考信任值高的使用者的意見來進行評分預測。然而這類方法在面對信任網路稀疏性的問題時，必須要透過信任傳遞的方法來擴充信任網路，其主要的限制在於在信任網路中沒有任何路徑相連的兩個使用者永遠沒辦法產生任何的信任關係，這將限制了這類方法的效能。為此，我們建立了一套信任預測機制來預測所有還沒出現過的信任關係的信任值，亦即是利用結構性預測器在信任網路中所產生的變數以及機器學習演算法來建立一個信任預測模型，我們採用了Epinions.com所提供的數據為實驗的資料集合，並與傳統的協同過濾推薦技術與採用信任傳遞之推薦技術相比，結果顯示我們的方法的確能夠提供比較高的預測涵蓋範圍(coverage)，同時，在比較相同預測目標的情況下，我們的方法也能提供比較高的預測準確度(prediction accuracy)。

As the emergence and rapid growth of Internet, it is more convenient for users to search for items (including information, products or services) via Internet. However, information explosion makes people difficultly assimilate large amounts of information. A solution to overcome this well-know information overload problem is to adopt a recommendation approach. The recommendation approach makes users searching items faster by suggesting the products they may like or be interested in and therefore could result in more consumption or usage of e-commerce websites for the enterprises. Recently, with the development of Web 2.0, the trust-based recommendation approach is the emerging recommendation approach. Based on the analysis of trust relationships, the trust-based recommendation approach finds the neighbors with highest trust values for an active user and then makes rating predictions based on their opinions. However, the trust-based recommendation approach suffers from the problem of trust network sparsity. The popular way is to adopt the trust propagation method to expand the trust network. Even so, the trust propagation method still has the limitation that two users have no chance to form a propagated trust relationship if there is no path in the trust network between them. In response, our proposed technique aims to develop a trust prediction mechanism to predict the trust value of each non-appeared trust relationship in the trust network. Specifically, we extract various structural predictors from a training dataset as the input variables for the machine learning algorithm to build the trust prediction model. We collect the evaluation dataset from Epinions.com and implement the benchmarks, i.e., the traditional collaborative filtering approach and the traditional trust-based recommendation approach for comparison. The experimental results demonstrate that our proposed technique can achieve higher coverage than that of the benchmarks. Moreover, our proposed technique outperforms the benchmarks on prediction accuracy for those items that can also be predicted by the benchmark techniques.

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