近年來，搜索引擎藉由機器學習技術與訓練查詢（training queries）來建立評等模型（ranking model），然後透過評等模型來進行網頁排序，其中訓練查詢來自過去使用者的查詢與點擊次數。目前，基於機器學習的排序學習方法非常多，其中以Raniking SVM最受矚目，然而此方法會因為大量的訓練資料而造成運算量過大。為了解決此問題，我們將評等支援向量機演算法修改成分散式的架構，並以雲端運算環境中的MapReduce來實現分散式演算法。MapReduce是由Google所提出，其隱藏平行處理的繁複細節、高容錯、資料分布、負載平衡，讓使用者可以簡單地使用大量的電腦來處理巨量資料集（large-scale datasets）。Ranking SVM主要分成訓練與測試兩個階段，在訓練階段，我們需要求解Ranking SVM的對偶子問題，而測試階段，則是藉由訓練後的評等模型對測試查詢作評分排名。因此，我們設計不同的MapReduce架構來滿足不同階段的需求。實驗結果證明，此分散式評等支援向量機可以有效地減少運算所耗費的時間。

Nowadays, search engines are more relying on machine learning techniques to construct a model, using past user queries and clicks as training data, for ranking web pages. There are several learning to rank methods for information retrieval, and among them ranking support vector machine (SVM) attracts a lot of attention in the information retrieval community. One difficulty with Ranking SVM is that the computation cost is very high for constructing a ranking model due to the huge number of training data pairs when the size of training dataset is large. We adopt the MapReduce programming model to solve this difficulty. MapReduce is a distributed computing framework introduced by Google and is commonly adopted in cloud computing centers. It can deal easily with large-scale datasets using a large number of computers. Moreover, it hides the messy details of parallelization, fault-tolerance, data distribution, and load balancing from the programmer and allows him/her to focus on only the underlying problem to be solved. In this paper, we apply MapReduce to Ranking SVM for processing large-scale datasets. We specify the Map function to solve the dual sub problems involved in Ranking SVM and the Reduce function to aggregate all the outputs having the same intermediate key from Map functions of distributed machines. Experimental results show efficiency improvement on ranking SVM by our proposed approach.

摘要 iv
Abstract v
圖目錄 viii
表目錄 ix
第一章簡介 10
1.1 研究背景 10
1.2 問題定義 12
1.3 研究目的 14
1.4 論文架構 15
第二章 文獻探討 16
2.1 雲端運算 16
2.2 Hadoop 17
2.2.1 Hadoop Distributed File System (HDFS) 18
2.2.2 MapReduce 20
2.3 虛擬主機 22
2.4 排序學習 23
2.4.1 逐點式方法（Pointwise Approach） 25
2.4.2 成對式方法（Pairwise Approach） 26
2.4.3 序列式方法（Listwise Approach） 28
2.5 Ranking SVM 28
2.6 評估工具 30
2.6.1 正規化遞減累積效益（NDCG） 30
第三章 研究方法 33
3.1 研究動機 33
3.2 方法概述 34
3.3 方法架構 36
3.3.1 架構一 36
3.3.2 架構二 39
3.3.3 架構三 42
3.3.4 架構四 45
第四章 實驗結果與分析 50
4.1 實驗資料 50
4.2 特徵選取 54
4.3 實驗環境架設 59
4.4 實驗結果與分析 59
4.4.1 實驗一 60
4.4.2 實驗二 63
4.4.3 實驗三 65
4.4.4 實驗四 66
4.4.5 實驗五 68
4.4.6 實驗六 72
4.4.7 實驗七 76
第五章 結果與未來展望 78
5.1 結論 78
5.2 未來研究方向 78
參考文獻 80
附錄1 82
Hadoop 0.20.2 叢集安裝 82

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