淡江大學機構典藏:Item 987654321/111162
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 62797/95867 (66%)
造访人次 : 3733304      在线人数 : 312
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/111162


    题名: 基於貝氏網路之圖書推薦系統
    其它题名: A book recommender system based on the Bayesian network
    作者: 劉羣冠;Liu, Chun-Kuan
    贡献者: 淡江大學資訊管理學系碩士班
    魏世杰;Wei, Shih-Chieh
    关键词: 貝氏網路;協同推薦;內容推薦;推薦系統;Bayesian Network;Collaborative Recommendation;content-based recommendation;Recommender System
    日期: 2016
    上传时间: 2017-08-24 23:45:31 (UTC+8)
    摘要: 本文將實做一個推薦系統,透過貝氏網路結合協同與內容推薦方法,可以在沒有使用者背景的情況下,依據「給定的一本書」,進行相關圖書的推薦,希望能增進使用者查詢借閱書籍之效率與提高圖書館的書本借閱率。因為目前一般圖書館少有推薦系統,且有推薦系統的圖書館大多針對使用者進行推薦,這樣使用者必須登入系統後才可有推薦清單。但使用者通常習慣在查詢書本時不想登入,以省去麻煩及遭追蹤的可能,所以希望可以透過大眾化書推薦書的方式,讓系統在不取得使用者背景的情況下進行推薦。
    本文透過離線實驗結果得出在貝氏網路架構下加入關鍵字與分類號節點資訊皆可提高其推薦結果且優於傳統推薦方法。本文也讓使用者進行線上實驗佐證本推薦方法的實用性。
    In this work, we will present a recommender system which combines the collaborative and content-based methods using the Bayesian network. Given a book, it can provide related book recommendation without knowing the user identity so that users can look up the desired books more easily and the library books can be utilized more efficiently. Currently, few libraries provide a book recommender system. For those libraries which provide the recommender system, the user must login to receive the recommended book list. But users are accustomed to looking up the books without login to save the trouble or the risk of being tracked. This work aims to provide an anonymous way of book recommendation about a book without knowledge of the user identity.
    In our offline experiments, the results show that under the framework of the Bayesian network, adding the keyword nodes and the subject code nodes can help promote the recommendation performance when compared with the traditional recommendation methods. This work also conducts an online user experiment to demonstrate the usefulness of our recommendation method.
    显示于类别:[資訊管理學系暨研究所] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML108检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈