博碩士論文 etd-0728108-174023 詳細資訊


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姓名 陳隆賢(Lung-Shian Chen) 電子郵件信箱 E-mail 資料不公開
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 96學年第2學期
論文名稱(中) 使用信任網路在電子商務環境中產生推薦
論文名稱(英) Employing Trust Network for Recommendation in e-Commerce
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    摘要(中) 身處於資訊超載的時代,許多人發現吸收資訊以及識別他們需要的資源是困難的。隨著電子商務的蓬勃發展,對消費者而言,在網路商店瀏覽、搜尋並且購買商品經常是一件費時且令人沮喪的事,許多想在電子商務網站購買商品的購物者,在離開網站時沒有發現他們想要的商品,因此,有很多電子商務網站實施推薦系統,打算為消費者提供各式各樣產品和服務的個人化推薦。近來有一些電子商務推薦系統的研究將社會影響納入考量,那些推薦系統可以達到較佳的預測正確性,而且可以克服以前方法的缺點。因此我們提出一個以信任網路為基礎的推薦架構,利用使用者之間的信任關係來產生推薦,我們使用了PageRank演算法來進行信任矩陣的調整以及產品推薦,另外,我們也提出了數個或許可以建立更佳信任矩陣的假設,並透過實驗來驗證,希望可以發現較好的方法來調整信任矩陣,最後我們提出了兩個方法來調整信任矩陣。針對推薦架構的每個階段,我們運用及評估信任矩陣調整和產品推薦方法的多種組合。使用Epinion.com資料的實驗結果顯示,針對不同的使用者群組使用不同的PageRank可以產生較佳的推薦結果,此外,我們提出了一個獲得最佳成果的混合方法。
    摘要(英) Living in the information-overloading age, many people find it difficult to assimilate the information and to identify resources they need. As to a consumer, browsing, searching, and buying a product on online stores is often a time-consuming and frustrating task with the flourishing development of e-commerce. Many shoppers who are interested in buying products on E-commerce websites end up finding nothing they want. Therefore, many E-commerce websites have implemented recommender systems that intend to provide consumers with personalized recommendations for various types of products and services. Some recent research has taken into account social influence in recommender systems in E-commerce. These recommender systems have been observed to achieve better accuracy of prediction, and have also overcome some of the problems of the previous methods. In this study, we propose a trust network-based recommendation framework that utilizes the trust relationship between users to generate recommendation. We employ PageRank algorithm for trust matrix adjustment and recommendation. In addition, we propose several assumptions that can be used to construct trust matrix, and we verify them by experiments. We finally identify two approaches for adjusting trust matrix. Bases on the trust and rating data collected from Epinion.com, we exercise several alternatives and evaluated many combinations of trust matrix adjustment and recommendation methods. Our experiment evaluation results show that using different pagerank for different users groups can generate better recommendation results. Moreover, we proposed a best hybrid method that achieves the best performance.
    關鍵字(中)
  • 電子商務
  • 推薦系統
  • 信任網路
  • 佩吉評比
  • 關鍵字(英)
  • Trust Network
  • Recommender System
  • E-Commerce
  • PageRank
  • 論文目次 CHAPTER 1 - Introduction 1
    1.1 Background 1
    1.2 Motivation 2
    1.3 Thesis Organization 3
    CHAPTER 2 - Literature Review 5
    2.1 Recommender Systems 5
    2.1.1 Content-based Methods 6
    2.1.2 Collaborative Methods 8
    2.2 Social Network Analysis 11
    2.2.1 The Elements of Social Network 12
    2.2.2 The Structural Properties of Social Network 13
    2.2.3 Graph Theory 13
    2.2.4 The Trust-Aware Recommendation 15
    CHAPTER 3 - Recommendation Approaches Based on a Given Trust Network 19
    3.1 Constructing the trust network 20
    3.2 Using the trust network to enable recommendation 21
    CHAPTER 4 - Experiment of Basic Approaches 25
    4.1 Data Collection 25
    4.2 Experiment and Result 26
    4.2.1 Preliminary Experiment 27
    4.2.2 Segmenting Users 29
    CHAPTER 5 - Extended Approach 33
    5.1 Asymmetric Approach 33
    5.2 Confidence Approach 35
    CHAPTER 6 - Experiments for Adjusted Trust Matrix 36
    6.1 Experiment using asymmetric trust assignment 36
    6.2 Experiment using confidence trust assignment 38
    6.3 Using both trust assignment 40
    6.4 Summary of Best Method of Segments 42
    CHAPTER 7 - Conclusion 47
    Reference 48
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    口試委員
  • 魏志平 - 召集委員
  • 蕭漢威 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2008-06-26 繳交日期 2008-07-28

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