博碩士論文 etd-0804106-143419 詳細資訊


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姓名 廖逸凡(Yi-fan Liao) 電子郵件信箱 m934020035@student.nsysu.edu.tw
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 94學年第2學期
論文名稱(中) 利用社會網路技術進行文獻資料庫的推薦
論文名稱(英) Employing Social Networks for Recommendation in a Literature Digital Library
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    摘要(中) 人際關係和推薦行為是目前日常生活中重要的關係和普遍存在的行為。身處於資訊超載的時代,雖然有著資訊搜尋的機制,卻因為使用者本身對於目標的不夠明確而使得搜尋的結果無法滿足讀者的需求。另外,隨著電子商務的蓬勃發展,網站的個人化與顧客導向需求成為一種趨勢,使得許多不同的推薦技術應運而生,推薦的型態也越來越多元。在眾多被提出的推薦方法之中以內容推薦方法(content-based recommendation approach) 和協同過濾推薦方法(collaborative filtering recommendation approach)是最成功且最常被採用的推薦技術。但是內容推薦方法沒有辦法辨別推薦項目之間的好與壞,只要項目的內容物組成份子相似,系統就會進行推薦。除了這些推薦技術本身存在著一些待克服的問題之外,現行的大部分推薦方法之中甚少利用人際關係這項因素來進行推薦,因此我們提出數個社會網路推薦方法(social network-based recommendation approach),利用推薦項目背後所隱藏的社會網路因素來判斷這些項目的相關性。我們將這些方法實證在文獻資料庫上的結果證明,當使用者現有感興趣的文章有高度內容相似性時,社會網路推薦方法能夠比內容推薦方法得到更準確的預測;但若使用者現有感興趣的文章內容迥異的話,則內容推薦方法可以得到較好的推薦結果。此外,利用社會網路的推薦方法可以避免前述內容推薦方法無法識別推薦項目好壞的問題。由於內容推薦方法和社會網路推薦方法所推薦的項目有相當的差異,實驗結果顯示,發展一個融合兩個方法的新方法可能可以得到更佳的推薦效果。
    摘要(英) Interpersonal relationship and recommendation are the important relation and popular mechanism. Living in the information-overloading age, the original information searching mechanisms, which require the specification of keywords, are ineffective and impractical. Moreover, a variety of recommendation techniques have been proposed and many of them have been implemented in real systems, especially in online stores. Among different recommendation techniques proposed in the literature, the content-based and collaborative filtering approaches have been broadly adopted by membership stores that maintain users’ long term interest. For short-term interest, by far the content-based approach is the most popular one for recommendation. However, most of the proposed recommendation approaches do not take the social information as an important factor. In this study, we proposed several social network-based recommendation approaches that take into account the similarities of items with respect to their social closeness for meeting users’ short term interests. Our experiment evaluation results show that social network-based approaches perform better than the content-based counterpart, if the user’s short term interest profile contains articles of similar content. Nonetheless, content-based approach becomes better when articles in the profile are diversified in their content. Besides, contrast to content-based approach, social network-based approach is less likely to recommend articles which readers do not value. Finally, the desired articles recommended by content-based approach are very different from those by social network-based approach. This suggests the development of some hybrid recommendation method that utilizes both content and social network when making recommendations.
    關鍵字(中)
  • 數位圖書館
  • 社會網路推薦方法
  • 推薦系統
  • 社會網路
  • 關鍵字(英)
  • social network
  • literature digital library
  • recommender system
  • social network-based recommendation approach
  • 論文目次 CHAPTER 1 INTRODUCTION 1
    1.1 BACKGROUND 1
    1.2 MOTIVATION 2
    CHAPTER 2 LITERATURE REVIEW 4
    2.1. SOCIAL NETWORK ANALYSIS 4
    2.1.1. The Elements of Social Network 4
    2.1.2. Structural Properties of Social Network 5
    2.1.3. Graph Theory 6
    2.1.4. The Exhibition of Social Network 7
    2.1.5. Link Prediction for Social Networks 8
    2.2. RECOMMENDER SYSTEMS 10
    2.2.1 Content-based Methods 12
    2.2.2 Collaborative Methods 14
    CHAPTER 3 SOCIAL NETWORK APPROACHES 18
    3.1 CONSTRUCTING THE SOCIAL NETWORK 19
    3.1.1. Maximum path weight 19
    3.1.2 Author Direct 25
    3.1.3 Structural Hole 25
    3.1.4 Author Cluster 27
    3.1.5 Direct closeness and Maximum indirect closeness 27
    3.2. RECOMMENDING LITERATURES 29
    3.2.1 Construction the Article-Author Matrix 29
    3.2.2 Constructing the Similarity between Articles 29
    3.2.3 Reference Point 30
    CHAPTER 4 EVALUATIONS 32
    4.1 DATA COLLECTION 32
    4.1.1 Processing the Co-authoring Data for Social Network Construction 32
    4.1.2 Processing the Content of Articles 33
    4.1.3 Experimental Design 34
    4.2 PERFORMANCE METRICS 34
    4.3 EXPERIMENTAL RESULTS 36
    4.3.1 Hit Rate 36
    4.4 FIDELITY 45
    CHAPTER 5 CONCLUSIONS 46
    REFERENCES 47
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    口試委員
  • 魏志平 - 召集委員
  • 楊婉秀 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2006-07-21 繳交日期 2006-08-04

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