Toggle navigation
Home
About
About Journal
Historical Evolution
Indexed In
Awards
Reference Index
Editorial Board
Journal Online
Archive
Project Articles
Most Download Articles
Most Read Articles
Instruction
Contribution Column
Author Guidelines
Template
FAQ
Copyright Agreement
Expenses
Academic Integrity
Contact
Contact Us
Location Map
Subscription
Advertisement
中文
Journals
Publication Years
Keywords
Search within results
(((CHU Zheng[Author]) AND 1[Journal]) AND year[Order])
AND
OR
NOT
Title
Author
Institution
Keyword
Abstract
PACS
DOI
Please wait a minute...
For Selected:
Download Citations
EndNote
Ris
BibTeX
Toggle Thumbnails
Select
Construction method of mobile application similarity matrix based on latent Dirichlet allocation topic model
CHU Zheng, YU Jiong, WANG Jiayu, WANG Yuefei
Journal of Computer Applications 2017, 37 (
4
): 1075-1082. DOI:
10.11772/j.issn.1001-9081.2017.04.1075
Abstract
(
389
)
PDF
(1175KB)(
550
)
Knowledge map
Save
With the rapid development of mobile Internet, how to extract effective description information from a large number of mobile applications and then provide effective and accurate recommendation strategies for mobile users becomes urgent. At present, recommendation strategies are relatively traditional, and mostly recommend applications according to the single attribute, such as downloads, application name and application classification. In order to resolve the problem that the granularity of recommended applications is too coarse and the recommendation is not accurate, a mobile application similarity matrix construction method based on Latent Dirichlet Allocation (LDA) was proposed. Started from the application labels, a topic model distribution matrix of mobile applications was constructed, which was utilized to construct mobile application similarity matrix. Meanwhile, a method for converting the mobile application similarity matrix to the viable storage structure was also proposed. Extensive experiments demonstrate the feasibility of the proposed method, and the application similarity achieves 130 percent increasement by the proposed method compared with that by the existing 360 application market. The proposed method solves the problem that the recommended granularity is too coarse in the mobile application recommendation process, so that the recommendation result is more accurate.
Reference
|
Related Articles
|
Metrics
Select
Parallel access strategy for big data objects based on RAMCloud
CHU Zheng, YU Jiong, LU Liang, YING Changtian, BIAN Chen, WANG Yuefei
Journal of Computer Applications 2016, 36 (
6
): 1526-1532. DOI:
10.11772/j.issn.1001-9081.2016.06.1526
Abstract
(
550
)
PDF
(1195KB)(
396
)
Knowledge map
Save
RAMCloud only supports the small object storage which is not larger than 1 MB. When the object which is larger than 1 MB needs to be stored in the RAMCloud cluster, it will be constrained by the object's size. So the big data objects can not be stored in the RAMCloud cluster. In order to resolve the storage limitation problem in RAMCloud, a parallel access strategy for big data objects based on RAMCloud was proposed. Firstly, the big data object was divided into several small data objects within 1 MB. Then the data summary was created in the client. The small data objects which were divided in the client were stored in RAMCloud cluster by the parallel access strategy. On the stage of reading, the data summary was firstly read, and then the small data objects were read in parallel from the RAMCloud cluster according to the data summary. Then the small data objects were merged into the big data object. The experimental results show that, the storage time of the proposed parallel access strategy for big data objects can reach 16 to 18 μs and the reading time can reach 6 to 7 μs without destroying the architecture of RAMCloud cluster. Under the InfiniBand network framework, the speedup of the proposed paralled strategy almost increases linearly, which can make the big data objects access rapidly and efficiently in microsecond level just like small data objects.
Reference
|
Related Articles
|
Metrics