TY - JOUR
T1 - The collaborative search by tag-based user profile in social media
AU - XIE, Haoran
AU - LI, Xiaodong
AU - WANG, Jiantao
AU - LI, Qing
AU - CAI, Yi
N1 - The research described in this paper has been supported by a Strategic Research Grant of the City University of Hong Kong (Project no. 7004046), the National Natural Science Foundation of China (Grant no. 61300137), the Guangdong Natural Science Foundation of China (no. S2013010013836), and the Fundamental Research Funds for the Central Universities, SCUT (no. 2014ZZ0035).
PY - 2014/6/11
Y1 - 2014/6/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84903640265&partnerID=8YFLogxK
U2 - 10.1155/2014/608326
DO - 10.1155/2014/608326
M3 - Journal Article (refereed)
C2 - 25692176
AN - SCOPUS:84903640265
SN - 2356-6140
VL - 2014
JO - Scientific World Journal
JF - Scientific World Journal
M1 - 608326
ER -