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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72145

    Title: 在Hadoop 環境下以自建本體 進行使用者興趣偵測與文件推薦;Automatically Constructing Ontology for Detecting User’s Interests and Document Recommendation Based on Hadoop Environment
    Authors: 趙濬;Chao,Chun
    Contributors: 資訊管理學系
    Keywords: 推薦系統;中文推薦系統;本體;分散式系統;Hadoop;使用者輪廓;Recommendation System;Chinese Recommendation System;Ontology;Distributed System;Hadoop;User Profile
    Date: 2016-07-19
    Issue Date: 2016-10-13 14:28:16 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 推薦系統是一種常見的資訊過濾系統,不論對於商業或是個人而言都是一項非常
    重要的技術。為了針對使用者做出客製化的推薦,時常會藉由使用者輪廓(User profile)
    Hadoop 分散式環境來提升系統效率與未來的可擴充性。
    在實驗的部分,本研究採用Amazon 網路書店的英文書籍簡介和博客來網路書店
    力處理更大量的文本資料。;Recommendation system is a common information filtering system. It’s (an
    important/a significant) technology for businesses and individuals. In order to make a
    customized recommendation for users, they usually record users’ past behaviors by using
    user profile. Throughout ontology to construct user profile recommended system can reach
    the recommendations of higher accuracy and diversity.
    This study mainly consists of two parts: Automatically constructs ontology and
    constructs user profile & recommendations. First of all, we input the English and Chinese
    documents separately into the system to construct ontology automatically. Next, the user
    behavior corresponds with ontology to construct user profile to go a step further of
    managements and recommendations. Moreover, this study has added agglomerative
    hierarchical clustering into the system to resolve the phenomenon of excessive clustering
    when constructed ontology in the past. To deal with the growth of information in the future,
    this study improves system efficiency and future scalability by using Hadoop distributed
    In the experiment part, this study adopts Amazon online shopping websites and Books
    online shopping websites as data collection, and simulates user’s interest variation under
    different conditions and languages to test our system recommendations’ quality. The result
    shows that we improve system recommendations’ quality successfully, and we are capable
    of handling massive texture data in the future.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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