The Collaborative Search by Tag-Based User Profile in Social Media
Recently, we have witnessed the popularity and proliferation of social media applications (e.g., Delicious, Flickr, and YouTube) in the web 2.0 era. The rapid growth of user-generated data results in the problem of information overload to users. Facing such a tremendous volume of data, it is a big...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/608326 |
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| _version_ | 1832551644630351872 |
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| author | Haoran Xie Xiaodong Li Jiantao Wang Qing Li Yi Cai |
| author_facet | Haoran Xie Xiaodong Li Jiantao Wang Qing Li Yi Cai |
| author_sort | Haoran Xie |
| collection | DOAJ |
| description | Recently, we have witnessed the popularity and proliferation of social media applications (e.g., Delicious, Flickr, and YouTube) in the web 2.0 era. The rapid growth of user-generated data results in the problem of information overload to users. Facing such a tremendous volume of data, it is a big challenge to assist the users to find their desired data. To attack this critical problem, we propose the collaborative search approach in this paper. The core idea is that similar users may have common interests so as to help users to find their demanded data. Similar research has been conducted on the user log analysis in web search. However, the rapid growth and change of user-generated data in social media require us to discover a brand-new approach to address the unsolved issues (e.g., how to profile users, how to measure the similar users, and how to depict user-generated resources) rather than adopting existing method from web search. Therefore, we investigate various metrics to identify the similar users (user community). Moreover, we conduct the experiment on two real-life data sets by comparing the Collaborative method with the latest baselines. The empirical results show the effectiveness of the proposed approach and validate our observations. |
| format | Article |
| id | doaj-art-fb897dddb35c4d2b9e3888c06ab2be20 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-fb897dddb35c4d2b9e3888c06ab2be202025-02-03T06:00:59ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/608326608326The Collaborative Search by Tag-Based User Profile in Social MediaHaoran Xie0Xiaodong Li1Jiantao Wang2Qing Li3Yi Cai4Department of Computer Science, City University of Hong Kong, Kowloon, Hong KongDepartment of Computer Science, City University of Hong Kong, Kowloon, Hong KongDepartment of Computer Science, Hong Kong Baptist University, Kowloon, Hong KongDepartment of Computer Science, Hong Kong Baptist University, Kowloon, Hong KongSchool of Software Engineering, South China University of Technology, Guangzhou 510006, ChinaRecently, we have witnessed the popularity and proliferation of social media applications (e.g., Delicious, Flickr, and YouTube) in the web 2.0 era. The rapid growth of user-generated data results in the problem of information overload to users. Facing such a tremendous volume of data, it is a big challenge to assist the users to find their desired data. To attack this critical problem, we propose the collaborative search approach in this paper. The core idea is that similar users may have common interests so as to help users to find their demanded data. Similar research has been conducted on the user log analysis in web search. However, the rapid growth and change of user-generated data in social media require us to discover a brand-new approach to address the unsolved issues (e.g., how to profile users, how to measure the similar users, and how to depict user-generated resources) rather than adopting existing method from web search. Therefore, we investigate various metrics to identify the similar users (user community). Moreover, we conduct the experiment on two real-life data sets by comparing the Collaborative method with the latest baselines. The empirical results show the effectiveness of the proposed approach and validate our observations.http://dx.doi.org/10.1155/2014/608326 |
| spellingShingle | Haoran Xie Xiaodong Li Jiantao Wang Qing Li Yi Cai The Collaborative Search by Tag-Based User Profile in Social Media The Scientific World Journal |
| title | The Collaborative Search by Tag-Based User Profile in Social Media |
| title_full | The Collaborative Search by Tag-Based User Profile in Social Media |
| title_fullStr | The Collaborative Search by Tag-Based User Profile in Social Media |
| title_full_unstemmed | The Collaborative Search by Tag-Based User Profile in Social Media |
| title_short | The Collaborative Search by Tag-Based User Profile in Social Media |
| title_sort | collaborative search by tag based user profile in social media |
| url | http://dx.doi.org/10.1155/2014/608326 |
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