博碩士論文 etd-0729112-222447 詳細資訊


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姓名 陳柏伶(Po-ling 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 100學年第2學期
論文名稱(中) 根據資訊需求模式進行旅遊文章的推薦
論文名稱(英) Recommending Travel Threads Based on Information Need
Model
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    摘要(中) 推薦技術的主要目的是希望能夠在大量的資訊中發掘使用真正的資訊需求。
    推薦系統幫助使用者過濾資訊以及嘗試呈現那些我們所感興趣的資訊供我們參考。
    在本篇論文裡,我們專注在旅遊領域中的討論串(threads)推薦。我們考慮當使用
    者有旅遊資訊需求的同時,他們會嘗試在網路上搜尋相關的資訊。除了瀏覽其他
    使用者的建議或意見時,人們可能也會將真正的需求表達成一個問題,希望能夠
    獲得其他人直接回覆。因此我們主要是根據使用者的瀏覽紀錄、輸入的問題以及
    目前所處的需求階段來推薦相似的問題並且已經有相關答案的討論串給使用者。
    我們提出了一個使用者模型,其中包含四個構面:目標相似度(goal similarity)、
    內容相似度(content similarity)、時間(freshness)以及品質(quality)。我們希望整合
    這四個構面可以提供有效的推薦結果。為了要驗證這四個構面以及推薦的績效,
    我們從網路上最大的旅遊網站-TripAdvisor-收集了 14348 篇文章,以及徵募了 10
    位對旅遊有興趣的自願者來做實驗。我們將這個四個構面分成兩個部分,第一個
    部分為 Question-based 方法,涵蓋了三個構面,內容相似度(content similarity)、
    時間(freshness)以及品質(quality),第二個部分是 Session-based 方法包含目標相
    似度(goal similarity)。並且也將這兩個方法合併,提出混合方法(hybrid method)。
    而實驗結果顯示混合方法(hybrid method)的結果比起兩個獨立方法可以提供相對
    較好的推薦結果。
    摘要(英) Recommendation techniques are developed to discover user’s real information
    need among large amounts of information. Recommendation systems help users filter
    out information and attempt to present those similar items according to user’s tastes. In
    our work, we focus on discussion threads recommendation in the tourism domain. We
    assume that when users have traveling information need, they will try to search related
    information on the websites. In addition to browsing others suggestions and opinions,
    users  are  allowed  to  express  their  need  as  a  question.  Hence,  we  focus  on
    recommending users previous discussion threads that may provide good answers to the
    users’ questions by considering the question input as well as their browsing records. We
    propose a model, which consists of four perspectives: goal similarity, content similarity,
    freshness  and  quality.  To  validate  and  the  effectiveness  of  our  model  on
    recommendation performance, we collected 14348 threads from TripAdvisor.com, the
    largest travel website, and recruited ten volunteers, who have interests in the tourism, to
    verify our approach. The four perspectives are utilized by two methods. The first is
    Question-based method, which makes use of content similarity, freshness and quality
    and the second is Session-based method, which involves goal similarity. We also
    integrate the two methods into a hybrid method.
    The  experiment  results  show  that  the  hybrid  method  generally  has  better
    performance than the other two methods.
    關鍵字(中)
  • 資訊需求模型
  • 問題推薦
  • 旅遊文章分類
  • 文字分類
  • 旅遊文章推薦
  • 關鍵字(英)
  • Text classification
  • Travel threads recommendation
  • Travel threads classification
  • Question recommendation
  • Information need model
  • 論文目次 TABLE OF CONTENTS
    CHAPTER 1    – Introduction ................................................................. 1
    1.1.   Background ................................................................................................... 1
    1.2.   Motivation .................................................................................................... 2
    1.3.   Thesis Organization ...................................................................................... 4
    CHAPTER 2    – Literature Review ....................................................... 5
    2.1.   Recommender Systems ................................................................................ 5
    2.1.1.    Collaborative Filtering Methods ....................................................... 6
    2.1.2.    Content-based Recommendation Methods ....................................... 8
    2.2.   Partially supervised classification ............................................................... 11
    2.3.   Travel threads classification ....................................................................... 14
    2.4.   Q&A Recommendation .............................................................................. 17
    CHAPTER 3    – The Model and Methods ........................................... 20
    3.1.   Perspective descriptions ............................................................................. 20
    3.1.1.    Goal similarity ................................................................................ 20
    3.1.2.    Content Similarity .......................................................................... 22
    3.1.3.    Freshness ........................................................................................ 23
    3.1.4.    Quality ............................................................................................ 24
    3.2.   Recommending Threads ............................................................................. 26
    3.2.1.    Question-based method .................................................................. 27
    3.2.2.    Session-based method .................................................................... 29
    3.2.3.    Hybrid method ................................................................................ 29
    CHAPTER 4    – Evaluation .................................................................. 30
    4.1.   Data Collection ........................................................................................... 30
    4.2.   Experiment Design ..................................................................................... 31
    4.3.   Performance Results ................................................................................... 33 
    4.3.1.    The MAE of the three methods ...................................................... 34
    4.3.2.    The impact of session size on MAE ............................................... 35
    4.3.3.    The impact of goal .......................................................................... 36
    CHAPTER 5    – Conclusions ................................................................ 39
    5.1.   Future work ................................................................................................ 40
    References  ................................................................................................ 41
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    口試委員
  • 江祥立 - 召集委員
  • 范俊逸 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2012-07-02 繳交日期 2012-07-29

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