|Author's Email Address
||This thesis had been viewed 5354 times. Download 17 times.|
|Type of Document
||Using Cloud Computing to Improve the Efficiency and Effectiveness of Matrix Factorization : A Case Study of Context-aware Data Set|
|Date of Defense
||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. .
||Yuh-Jiuan Tsay - chair|
Bing-Chiang Jeng - co-chair
Wei-Po Lee - advisor
Indicate in-campus at 2 year and off-campus access at 2 year.|
|Date of Submission