International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 24 - 35

Personalized Tag Recommendation Based on Convolution Feature and Weighted Random Walk

Authors
Liu Zheng1, 2, *, Zhao Tianlong1, 2, Han Huijian1, 2, Zhang Caiming3, 4
1School of Computer Science and Technology, Shandong University of Finance and Economics, Ji'nan, 250014, China
2Shandong Provincial Key Laboratory of Digital Media Technology, Ji'nan, 250014, China
3Software College, Shandong University, Ji'nan, 250101, China
4Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, 264025, China
*Corresponding author. Email: Lzh_48@126.com
Corresponding Author
Liu Zheng
Received 31 August 2019, Accepted 24 December 2019, Available Online 21 January 2020.
DOI
10.2991/ijcis.d.200114.001How to use a DOI?
Keywords
Flickr; User group; Bipartite graph; Weighted random walk; Personalized tag recommendation
Abstract

Automatic image semantic annotation is of great importance for image retrieval, therefore, this paper aims to recommend tags for social images according to user preferences. With the rapid development of the image-sharing community, such as Flickr, the image resources of the social network with rich metadata information demonstrate explosive growth. How to provide semantic tagging words (also known as tag recommendation) to social images through image metadata information analysis and mining is still a question, which brings new challenges and opportunities to the semantic understanding of images. Making full use of metadata for semantic analysis of images can help to bridge the semantic gap. Thus, we propose a novel personalized tag recommendation algorithm based on the convolution feature and weighted random walk. Particularly, for a given target image, we select its visual neighbors and determine the weight of each neighbor by mining the influence of user group metadata in Flickr on image correlation, and combining group information and visual features extracted by Convolutional Neural Network (CNN). Afterwards, the weighted random walk algorithm is implemented on the neighbor-tag bipartite graph. Experimental results show that tags recommended by our proposed method can accurately describe the semantic information of images and satisfy the personalized requirements of users.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
24 - 35
Publication Date
2020/01/21
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200114.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Liu Zheng
AU  - Zhao Tianlong
AU  - Han Huijian
AU  - Zhang Caiming
PY  - 2020
DA  - 2020/01/21
TI  - Personalized Tag Recommendation Based on Convolution Feature and Weighted Random Walk
JO  - International Journal of Computational Intelligence Systems
SP  - 24
EP  - 35
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200114.001
DO  - 10.2991/ijcis.d.200114.001
ID  - Zheng2020
ER  -