Title page for etd-0619117-145750


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URN etd-0619117-145750
Author Che-wen Ku
Author's Email Address No Public.
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Department Information Management
Year 2016
Semester 2
Degree Master
Type of Document
Language English
Title Topic Recommendation and Discovery based on Matrix Factorization
Date of Defense 2017-07-13
Page Count 49
Keyword
  • Non-negative Matrix Factorization
  • Topic Discovery
  • Recommendation
  • Topic Modeling
  • Abstract Nowadays, there are more and more text documents on the Internet with the development of the Internet, because much information is related to text. Thus, researchers have used these text documents for text analysis. Non-negative Matrix Factorization is a kind of non-probabilistic method to factorize the matrix. In this thesis, we propose to use sparse-constraint NMF to do topic modeling with k topics. Moreover, we want to incorporate author information into nsNMF and so as to find hidden parts in the topics. It can offer more information and make the topic more concentrated. Among it, how many topic k is also a critical but difficult issue. Here, we use the mutual information and stability to determine the number of topic k. Besides, we want to find the changes of terms in topics in different time using Jensen-Shannon divergence and use Hungarian algorithm to match the topics in different times.
    Advisory Committee
  • Keng-pei Lin - chair
  • Pei-ju Lee - co-chair
  • Yihuang Kang - advisor
  • Files
  • etd-0619117-145750.pdf
  • Indicate in-campus at 1 year and off-campus access at 2 year.
    Date of Submission 2017-07-19

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