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Title page for etd-1120112-160851


URN etd-1120112-160851 Statistics This thesis had been viewed 1132 times. Download 6 times.
Author Chen-Jen Chen
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2012 Semester 1
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 67
Title Collaborative Approach to Recommending Point of Interest
Keyword
  • POI
  • Recommender System
  • Collaborative
  • Collaborative
  • Recommender System
  • POI
  • Abstract Due to the rapid development of global networks, result in a large number of Point Of Interest, POI, are full of the whole online world, and POI is that people have points of interest or useful or locations [35], in order to find what users want efficiently, recommender system is be created by this way.
    Recommender systems can be divided into three broad, respectively, are Content-Based Recommender System [11][23], Collaborative Filtering Recommender System [10][16][18][23][33] and the Hybrid Recommender System [19][23], By means of information, such as user preferences or past experience to recommend effective POI to the user.
    Nowadays, more and more users communicate with friends and share knowledge in the major Social Network Site, SNS, unwittingly, formed the knowledge which is generated as "Collaborative". However, current SNS are focused on the creation, presentation and management of the page, but lacking support of content. This is because the Web page by using the HTML markup language, mainly content layout design, presents a human readable article, rather than procedural automation to provide machine-readable data calculation and inferences.
    In this study is based on Drupal [25] to establish platform of recommended POIs, combined SNS let recommended POIs formation collaborative cooperation of mode, and purpose the method of " Facebook-Based Collaborative Filtering, FBCF ", allows users to enhance reliability of POI with other friends interest, and combined semantic web technology to let content information no longer just a page rendering, but supporting computer certain extent of calculation and inferences.
    Advisor Committee
  • Ching-Long Yeh - advisor
  • Yao-Ming Yeh - co-chair
  • Yue-Sun Kuo - co-chair
  • Files indicate in-campus access immediately and off-campus access at 5 years
    Date of Defense 2012-11-07 Date of Submission 2012-11-20


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