推薦系統是近年來相當熱門的研究主題。常見的做法包含協同式推薦系統及內容式推薦系統。協同式推薦系統受限於資料過於稀疏(data sparsity)以及冷啓動(cold start)兩個問題。內容式推薦系統則多侷限於使用者既有的興趣，較難針對推薦系統的目標函數做最佳化。內容式推薦系統雖然沒有協同式推薦系統的效果好，但是卻能夠幫助協同式推薦系統解決冷僻問題之窘境。因此也有許多研究，綜合兩者之方法，以截長補短的方式進行推薦。 因此在這篇論文中，我們提出一混和模型，以CCAM共分群演算法整合協同式推薦系統及內容式推薦系統，以解決cold start以及data sparsity的問題。CCAM是一基於information theoretic co-clustering的共分群演算法，但考慮了額外的內容資訊，像是使用者的特性資料及產品的特徵等。最後，我們將此混和模型實作於Hadoop Map-Reduce平台，以期望發展一兼顧效率與效能之推薦系統。 ;Recommender systems have become an essential research field because of a high interest from academia and industries. Collaborative filtering (CF), a branch of recommender system, is frequently confronted with the sparsity issue (resulted in fewer records (rating / clicking) against the unknowns that need to be predicted) and “cold start” problem (hard to make prediction for new user and new item), while Content-based (CB) approaches are limited by recommending similar items without user-item click information. Empirically, CF is better than CB, but is helpful to solve cold-start problem. Therefore, many hybrid approaches have been proposed to integrate collaborative filtering and content-based approach. In this thesis, we propose a hybrid approach that combines content-based approach with collaborative filtering under a unified model called co-clustering with augmented matrices (CCAM). CCAM is based on information theoretic co-clustering but further considers augmented matrices like user profile and item description. We then build a collaborative filtering model based on content-based information and co-clustering result to reduce the sparsity problem and solve cold-start problem. Finally, a parallel approach is proposed to solve the scalability problem of large data set.