博碩士論文 etd-0731115-161151 詳細資訊


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姓名 廖志倫(Chih-lun Liao) 電子郵件信箱 E-mail 資料不公開
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 104學年第1學期
論文名稱(中) 效率改良協同式過濾推薦系統
論文名稱(英) Efficiency Improvement for Collaborative Filtering Recommender System
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    紙本論文:5 年後公開 (2020-08-31 公開)

    電子論文:使用者自訂權限:校內 5 年後、校外 5 年後公開

    論文語文/頁數 中文/58
    統計 本論文已被瀏覽 5640 次,被下載 26 次
    摘要(中) 在協同過濾推薦系統中,使用者會對他們所購買的產品提供評分,藉由學習使用者們過去的交易、評分行為,推薦系統演算法可以推薦符合使用者們喜好的產品給使用者們。然而,產品的數目通常十分龐大,這也導致推薦系統的整體效能非常低落,因為在推薦每一件產品前,都需要考慮該名使用者對所有產品的喜好程度。因此,我們提出了一個新方法,將自建構分群演算法應用於推薦系統,以減少與產品的數量相關的維度,並藉此提升整體效能。其概念是將相似的產品集中在一個產品群;而不相似的產品則分到不同的產品群。在我們的方法中,推薦系統在運算過程中,可以改為以產品群為單位進行運算。最後,再透過反轉換,將產品群喜好列表,轉回產品喜好列表,並提供給每名使用者。我們所提出的方法,能使推薦系統的整體運算時間大幅的減少,並保持原先的高準確度。實驗結果証明,我們的推薦系統在效能上,較未經過維度縮減的推薦系統優良。
    摘要(英) In collaborative filtering based recommender systems, products are regarded as features and users are required to provide rating scores to the products they have purchased. By learning from the rating scores, such a recommender system can recommend interesting products to the users. However, there are usually quite a lot of products involved and it would be very inefficient if every product needs to be considered before making any recommendations. We propose a novel approach which applies a self-constructing clustering algorithm to reduce the dimensionality related to the number of products. Similar products are grouped in a cluster and dissimilar products are dispatched in different clusters. Recommendation work is then done with the resulting clusters. Finally, re-transformation is performed and a preference list about the products is offered to each user. With the proposed approach, the processing time for making recommendations is much reduced. Experimental results show that the efficiency of the recommender systems is greatly improved without the degradation of the recommendation quality.
    關鍵字(中)
  • 排名演算法
  • 分群
  • 特徵萃取
  • 關聯圖
  • 推薦系統
  • 關鍵字(英)
  • ranking algorithm
  • clustering
  • feature extraction
  • correlation graph
  • Recommender system
  • 論文目次 中文論文審定書+i
    英文論文審定書+ii
    誌謝+iii
    中文摘要+iv
    ABSTRACT+v
    CONTENTS+vi
    LIST OF FIGURES+viii
    LIST OF TABLES+ix
    Chapter 1 Introduction+1
    1.1  研究背景+1
    1.2 推薦系統+2
    1.3 問題描述+6
    1.4 論文架構+6
    Chapter 2 文獻回顧+8
    2.1 近年的內容導向的推薦系統介紹+8
    2.2 近年的協同式過濾推薦系統介紹+9
    2.3 ItemRank+12
    Chapter 3 基於分群的協同過濾式推薦系統+14
    3.1  自建構分群演算法(Self-Constructing Clustering)+15
    3.2 第一階段:使用者標籤+19
    3.3 第二階段:維度縮減+20
    3.4 第三階段:建立關聯圖+22
    3.5 第四階段:隨機移動+24
    3.6 第五階段:反轉換+25
    3.7 範例+27
    Chapter 4 實驗結果+32
    Chapter 5 結論與未來方向+41
    5.1 結論+41
    5.2 未來方向+41
    REFERENCE+42
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  • 口試日期 2015-08-27 繳交日期 2015-08-31

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