Title page for etd-0221117-203746


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URN etd-0221117-203746
Author Shih-hao Wang
Author's Email Address No Public.
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Department Information Management
Year 2016
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Using Cloud Computing to Improve the Efficiency and Effectiveness of Matrix Factorization : A Case Study of Context-aware Data Set
Date of Defense 2017-01-23
Page Count 60
Keyword
  • Context Aware
  • Matrix Factorization
  • Cloud Computing
  • Recommended System
  • Collaborative Filtering
  • Abstract There are two types of methods often used to develop collaborative recommender systems. One is based on the similarity calculation and the other is based on matrix factorization.
    Although the matrix factorization method performs better than the similarity-based method, it has to solve the time-consuming problem. Cloud computing can let some problem which need a lot of time become shorter than normal.
    This study develops an approach that uses Hadoop’s HDFS and Spark to improve the performance of matrix factorization. By using the presented approach, the computational time for matrix factorization can be largely reduced. .
    Advisory Committee
  • Yuh-Jiuan Tsay - chair
  • Bing-Chiang Jeng - co-chair
  • Wei-Po Lee - advisor
  • Files
  • etd-0221117-203746.pdf
  • Indicate in-campus at 2 year and off-campus access at 2 year.
    Date of Submission 2017-03-21

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