臺灣博碩士論文加值系統

推薦系統發展至今已產生多種不同的技術，大致上主要可分為：內容式過濾、協同式過濾、人口特徵過濾這三種，但是這些技術在個別使用上，都分別有一些潛在的問題存在。有鑑於此，已有不少的學者，提出改以數種推薦方式混合使用來進行，目的希望能夠截長補短，降低單一方法的缺點，以達到提供更精確的推薦。

然而目前大部分被使用的混合方式，大多是根據過去的研究經驗，選擇行之有效的啟發式方法來進行混合推薦，缺乏嚴謹的理論基礎。因此本研究嘗試運用推薦技術的概念，分別以內容式過濾、協同式過濾，以及人口特徵過濾，將這些推薦方法結合資料探勘技術，資料探勘分類技術能夠從龐大的資料中，挖掘出各個項目之間隱藏性的知識與規則，並建立資料屬性與類別之間的關係模型，進而以此關係模型做更有效的預測。

考量到不同資料探勘技術所展現出來的成效也許會有差異，因此本研究將同時使用線性回歸與類神經網路兩種演算法，來建立一套新的混合推薦系統預測模型，並比較不同推薦技術之間的成效，找出最佳的混合推薦模型。

Nowadays, the Recommender System has been developed in several different ways for operating. The main techniques are used to develop Recommender System: CB (Content-Based), CF (Collaborative Filtering) and DF (Demographic Filtering). However, each technique has its advantages and limitations. For this reason, many scholars have proposed combine several techniques, intended to reduce the disadvantages of a single method, and achieve more precise recommendation.

Currently, the main techniques are used to develop Recommender System, mostly according to the experience of the past research or heuristic method. It lacks of rigorous theoretical foundation. Therefore, this study hopes to use the concepts of CB and CF, plus DF techniques, combining the Data Mining techniques (i.e., Linear Regression, Neural Networks) with the predication. To sum up, it will provide a more accurate prediction than one single technique, and overcome the limitations of each respective potential problem.

中文摘要....................................i
英文摘要...................................ii
內容目錄...................................iv
第一章 緒論...............................1
第一節 研究背景........................1
第二節 研究動機........................2
第三節 研究目的........................4
第四節 研究對象........................4
第五節 研究流程........................5
第六節 研究架構........................6
第二章 文獻探討............................7
第一節 推薦技術........................7
第二節 資料探勘技術....................11
第三節 混合式推薦系統..................16
第四節 混合式推薦系統相關文獻............21
第三章 研究方法...........................23
第一節 系統架構.......................23
第二節 系統評估.......................32
第四章 實驗設計.............................35
第一節 實驗環境與工具....................35
第二節 實驗設計.........................35
第三節 系統建立.........................41
第五章 實驗結果與評估分析.....................50
第一節 單一與混合模型平均絕對誤差比較.......51
第二節 單一與混合模型精確度比較............53
第三節 單一與混合模型喚回率比較............55
第四節 單一與混合模型F-MEASURE值比較......57
第五節 單一與混合模型準確率比較............59
第六章 結論及未來研究方向...................61
第一節 結論..........................61
第二節 未來研究方向....................63
參 考 文 獻................................64

一、英文部分
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二、網頁部份
MovieLens網站(2012)，(檢索日期2012/December) http://www.grouplens.org/node/73