title: The collaborative search by tag-based user profile in social media.
abstract: 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.
authors.affiliation: Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.
authors.forename: Haoran
authors.identifier: 0000-0003-0965-3617
authors.initials: H
authors.lastname: Xie
authors.affiliation: Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong.
authors.forename: Jiantao
authors.identifier: 0000-0001-6732-5561
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong ; Multimedia Software Engineering Research Centre, City University of Hong Kong, Kowloon, Hong Kong.
authors.forename: Qing
authors.initials: Q
authors.lastname: Li
authors.affiliation: School of Software Engineering, South China University of Technology, Guangzhou 510006, China.
authors.forename: Yi
authors.identifier: 0000-0002-9798-2463
authors.initials: Y
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D003299:Cooperative Behavior; D057225:Data Mining / Q000379:methods*; D061108:Social Media