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研究生:詹尚驥
研究生(外文):Shang-Chi Chan
論文名稱:個人化文章自動推薦-以學術文獻部落格為例
論文名稱(外文):The Personalized Autorecommendation for Research Literature Blog
指導教授:李麗華李麗華引用關係
指導教授(外文):Li-Hua Li
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:97
語文別:中文
論文頁數:104
中文關鍵詞:部落格推薦系統類神經網路本體論自適應共振理論網路
外文關鍵詞:BlogRecommendation System (RS)Artificial Neural Network (ANN)Adaptive Resonance Theory (ART)Ontology
相關次數:
  • 被引用被引用:3
  • 點閱點閱:683
  • 評分評分:
  • 下載下載:142
  • 收藏至我的研究室書目清單書目收藏:1
依據美國部落格搜尋網站公司Technorati[44]於2007年的報告指出,部落格網站的數量已快速攀升至7,200萬且越來越受到許多部落客的青睞,也因此讓許多部落格網站使用者逐漸地轉型成部落客(Blogger)。然而,隨著部落格資訊的增長,也產生了現今大多數部落格網站無法避免的文章資訊過載(Information Overloading)現象。因此,如何有效協助資訊之查找,如何有效的推薦文章,已成為不可或缺的個人化服務,這也是目前多數的部落格網站所缺乏的。
本研究為能有效推薦符合瀏覽者需求的文章且能克服上述問題,因此,提出一個部落格網站系統平台,命名為BARS(Blog Article Recommendation System),BARS可以用來分析瀏覽者的偏好興趣,並將瀏覽者的偏好興趣紀錄依本體論(Ontology)階層概念建構出樹狀型態,用以表達瀏覽者的歷史閱覽記錄及挖掘潛在興趣偏好,此外,本研究亦利用類神經網路之模型—自適應共振理論網路(Adaptive Resonance Theory, ART)來將具有相同偏好興趣的瀏覽者進行分群,分群後將具有相似鄰居性質的瀏覽者透過推薦系統(Recommendation System, RS)中的協同過濾法(Collaborative Filtering, CF)來找出目標瀏覽者與其他鄰近瀏覽者相似的潛在偏好。針對那些沒有相似鄰居群的目標瀏覽者,則透過推薦系統的另一項方法--內容導向法(Content-Based method, CB),依瀏覽者過去的瀏覽歷史記錄及本體論概念的推論方式來推論出使用者未曾瀏覽過卻可能產生的興趣偏好,藉此亦可降低冷啟始(Cold-start)所產生因起始資料不多而無法推薦的問題。基於上述研究理論,本研究欲達成的目標及貢獻為:(1) 解決因資訊過載、文章資訊搜集繁雜議題,(2) 降低瀏覽者冷啟始問題,(3) 提昇個人化之文章推薦效能,(4) 提出部落格推薦應用之先導研究。本研究依照上述方法已完成系統建置與實驗,本研究運用三種推薦方式並與傳統的非個人化推薦方法(NPRL)做比較,這三種方法為BRL(Browser-based Recommendation List)、CRL(Cluster-based Recommendation List)及SCRL(Spread Category Recommendation List)。實驗結果顯示使用本研究BARS系統所提之BRL方法,其文章推薦命中率可達到84%;在SCRL方法上也可獲得83%的推薦命中率,而第三個方法CRL雖是三個方法中較低的,但仍具有高達80%的文章推薦命中率,上述三種方法都較傳統的NPRL方法之效能更佳。
According to Technorati’s [44] 2007 report, the blog sites is up rising to 72 millions and the popularity of bloggers has drawn many attention. This phenomenon has turned many web users into bloggers. The vast amount of blog information also brings the phenomenon of information overloading which is not handled by the blog function yet. In addition, the personalized recommendation service, which should be provided, is also not incorporated in the blog function now.
To better service the bloggers and to overcome the above problems, this research proposes a Blog Article Recommendation System (BARS) which provides personalized article recommendation based on blogger’s preference.
This research adopts the ontology technique in BARS to construct a personal preference tree for recording blogger’s interests and for further inference. The ART (Adaptive Resonance Theory) network is also utilized to cluster the group of similar interests. In order to find the similar preference between target blogger and the corresponding neighbors, this research applies the Collaborative Filtering (CF) technique to generate the recommendation. The “cold-start” problem, i.e. lacking of blogger’s usage data at the very beginning, is handled by integrating ontology and Content-Based (CB) filtering method to infer the potential preference in BARS. The purpose of this research is to achieve the followings.
(1) To solve the problem of information overloading.
(2) To handle the cold-start problem when making the recommendation.
(3) To implement BARS for fulfilling the personalized blog-article recommendation.
(4) To act as the pioneer for providing recommendation service in blog’s research.
To demonstrate the proposed method, this research has implemented the BARS as an academic blog for real-world experiment. Experiments are done by collecting real bloggers’ usage and their feedbacks so that the recommendation correctness is verified. Three types of recommendation approach are proposed and compared with the traditional Non-personalized recommendation list (NPRL), these three types of approach are BRL (Browser Based Recommendation List), SCRL (Spread Category Recommendation List), and CRL (Cluster Based Recommendation List) method. The experiment shows that BRL produces better recommendation result than SCRL and CRL with 84% satisfaction by all the bloggers.
摘 要 i
Abstract iii
誌謝 v
目錄 vii
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 導論 1
1.2 研究目的 5
1.3 研究範圍 6
1.4 論文架構 7
第二章 文獻探討 9
2.1 部落格(Blog) 10
2.1.1 部落格的定義 10
2.1.2 部落格的現況 10
2.1.3 小結 11
2.2 推薦系統(Recommendation System, RS) 11
2.2.1 推薦系統簡介 12
2.2.2 推薦系統機制 14
2.2.2.1 內容導向式推薦(Content-Based Filtering, CB) 15
2.2.2.2 協同過濾式推薦(Collaborative Filtering, CF) 16
2.2.3 推薦系統應用 19
2.2.4 小結 21
2.3 類神經網路(Artificial Neural Network, ANN) 22
2.3.1 自適應共振理論網路(Adaptive Resonance Theory, ART) 23
2.4 本體論(Ontology) 29
2.4.1 本體論概念推論 31
2.4.2 本體論推薦系統相關研究 35
2.5 詞彙分析相關技術及方法 35
2.5.1 CKIP 中文斷詞系統 36
2.5.2 TF operation 39
第三章 研究方法 41
3.1 BARS系統架構 42
3.2 文章分類模組(Article Classifier Module, ACM) 42
3.3 本體論建構模組(Ontology Construction Module, OCM) 47
3.4 文章推薦模組(Article Recommendation Module, ARM) 51
3.5 推薦效能評估 67
第四章 實驗設計與分析 69
4.1 實驗資料與環境設定 69
4.2 資料研析 71
4.3 實驗流程與成果 73
4.3.1 文章分類模組(Article Classifier Module, ACM) 73
4.3.2 本體論建構模組(Ontology Construction Module, OCM) 77
4.3.3 文章推薦模組(Article Recommendation Module, ARM) 79
4.4 推薦效能評估 83
4.4.1 BRL之效能評估 84
4.4.2 SCRL之效能評估 85
4.4.3 CRL之效能評估 86
4.4.4 NPRL之效能評估 87
4.4.5 四種推薦方法之效能比較 88
4.4.6 Top-1到Top10四種推薦方法之效能比較 92
第五章 結論與未來研究 95
5.1 研究貢獻 96
5.2 未來研究 97
參考文獻 99

表目錄
表一 文章推薦系統相關研究整理 20
表二 三種文章推薦系統的方法比較與整理 21
表三 自適應共振理論網路演算法相關變數 26
表四 本體論推薦系統相關研究 34
表五 中研院平衡語料庫詞類標記集 (資料來源:[1]) 38
表六 協同過濾法推薦範例 58
表七 瀏覽者文章推論流程圖相關變數說明 66
表八 學術文獻日誌類別主題 71
表九 瀏覽者瀏覽歷史記錄 (列舉) 72
表十 瀏覽者歷史記錄描述 72
表十一 詞庫對映結果(資料探勘類) (列舉) 75
表十二 文章分類結果 (列舉) 76

圖目錄
圖一 論文架構圖 8
圖二 協同過濾式推薦處理程序 (資料來源:[41]) 17
圖三 自適應共振理論網路架構 (資料來源:[5]) 25
圖四 Quickstep推論公式 (資料來源:[35]) 32
圖五 個人輪廓偏好推論公式 (資料來源:[3]) 33
圖六 CKIP系統斷字前畫面 (資料來源:[1]) 39
圖七 CKIP系統斷字後結果 (資料來源:[1]) 39
圖八 部落格文章推薦系統(BARS)架構圖 43
圖九 CKIP system文章斷字流程 44
圖十 詞彙庫對映流程圖 47
圖十一 建構OPPT流程圖 49
圖十二 OPPT (列舉範例) 50
圖十三 ARM推薦機制流程圖 52
圖十四 ART矩陣編碼範例 (列舉範例) 55
圖十五 協同過濾法推薦範例 57
圖十六 Top-N文章推論流程 65
圖十七 學術文獻部落格首頁 70
圖十八 斷詞後包含全部詞類標記結果 (列舉) 74
圖十九 斷詞後僅包含本研究所需詞類結果 (列舉) 74
圖二十 Top-10最受歡迎的文章類別 77
圖二十一 本體論個人偏好樹(OPPT) (列舉範例) 78
圖二十二 ART矩陣編碼 (列舉範例) 80
圖二十三 警戒值與分群群數測試 81
圖二十四 警戒值0.4之分群結果 82
圖二十五 BRL之效能評估實驗結果 85
圖二十六 SCRL之效能評估實驗結果 86
圖二十七 CRL之效能評估實驗結果 87
圖二十八 NPRL之效能評估實驗結果 88
圖二十九 文章推薦準確率比較圖 90
圖三十 文章推薦回應率比較圖 90
圖三十一 文章推薦F1指標比較圖 91
圖三十二 四種文章推薦方法比較圖 91
圖三十三 Top-10 Precision比較圖 93
圖三十四 Top-10 Recall比較圖 93
圖三十五 Top-10 F1比較圖 94
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