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| The Research of Broadcast Television Community Discovery Technology Based on Double-weight Gaussian Kernel Similarity |
| Xin Wang, Fulian Yin, Jianping Chai, Xinran Wang |
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Abstract The community discovery technology is proposed based on the
double-weight Gaussian kernel similarity to meet the need of
intellectual precise delivery business of broadcast television. The
text proposes a community technique based on the double-weight
Gaussian kernel similarity, according to the user rating data and
program broadcast data. It takes user preferences of program
category as nodes and the similarity of different nodes as edges to
build the diagram of TV broadcast community. In order to realize the
segmentation of TV users, it adopts the spectral clustering
algorithm and transfer the diagram into the matrix of user
preferences of program category. In addition, it proposes Gaussian
kernel similarity algorithm based on double-weight. For the
multivariate data, such as Wine data and Heart data, its veracity
can be enhanced by 2\% according to the similarity based on the
attribute character and the spatial character of the data. Checking
the community discovery technique based on double-weight Gaussian
kernel similarity, the result shows that it can find out user
communities of different preferences of program category, converge
to the global optimal situation and simplify the computational
complexity.
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| DOI:10.12733/jics20105669 |
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