In the age of information explosion, Internet facilitates product searching and collecting much more convenient for users. However, it is time-consuming and exhausting for users to deal with large amounts of product information. In response, various recommendation approaches have been developed to recommend the products that match the users’ preferences and requirements. In addition to the famous collaborative filtering recommendation approach, the trust-based recommendation approach is the emerging one. The reason is that most of the online communities allow users to express their trust on other users. Based on the analysis of trust relationships, the trust-based recommendation approach finds out and consults the opinions of more reliable users and therefore makes better recommendations. However, the existing trust-based recommendation approaches are restricted by the information sources so that these techniques only take the existence of trust relationship into account. Specifically, all the existing trust relationships are deemed equally important and given the same trust strength. Therefore, it is not a reasonable treatment in the real situation. Based on this consideration, we propose a mechanism of trust strength estimation by using the machine learning algorithm to give the corresponding trust strength for each existing trust relationship in the trust network. To overcome the sparsity of the trust network, we also develop the modified trust propagation method to expand the original trust network. Finally, we perform a series of experiments to demonstrate the performance of our trust-based recommendation approach based on the trust strength estimation mechanism. The results show that our proposed approach outperforms the benchmarks, i.e., the traditional collaborative filtering approach and the original trust-based one.

在這個資訊爆炸的時代，雖然網路可以讓使用者快速方便地搜尋及獲取想要的商品，但充斥於網路中的龐大商品資訊，卻使得使用者必須付出大量的時間與精力去過濾這些資訊。為此，推薦系統便因應而生，其目的在於藉由推薦與使用者喜好相符的商品，來避免資訊過量的問題。除了協同式過濾推薦方式外，另外一種以信賴關係為基礎的推薦方式也開始流行，這是由於目前有許多的線上社群均已允許使用者表達對其他使用者的信任，透過信任關係的分析，我們將更容易參考值得信賴的使用者來達到更高的推薦效能。然而，這類方法受限於資料來源，只能考慮信任關係的有無，亦即是所有存在的信任關係都被視為是一樣的強度，這一點在實際的環境中是不盡合理的，將會導致推薦效能的降低。基於這個考量，本研究發展出一套利用機器學習演算法估計信任強度的機制，讓信任網路中存在的信任關係都會有自己的相對應的信任強度，此外，為了避免信任網絡稀疏性的問題，我們也採用了信任遞移的觀念將信任網絡加以擴充。透過實驗，我們驗證了利用所開發的信任強度預測機制來進行推薦，其推薦效能勝過傳統的協同式過濾推薦方法以及只考慮單一信任強度的信任關係推薦方法。

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