TY - GEN
T1 - CAIS
T2 - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
AU - Huang, Han Chang
AU - Kao, Hung-Yu
PY - 2011/9/19
Y1 - 2011/9/19
N2 - Folksonomy systems provide a way for users to share and organize bookmarks. The social relationship among users has become stronger with the rapid development of new technologies. Finding the leading objects has become an important topic. These research topics are always centered around finding the most popular pages or experts. In this paper, we propose a new notion of expertise, which we call user insight. User insight denotes the user's expertise in finding Web pages that are useful or have the potential to be popular pages before other users find them. To address the issue, we refer to three major types of Web pages, namely, isolated, well-known, and burgeoning. Burgeoning pages are exceptionally useful and attractive for users in a folksonomy system. In our paper, we build a time-based algorithm to estimate user insight. In addition, we discuss the social relationship within fan networks, and we propose a link-based algorithm called CAIS (Community-based Annotation Insight Search) to realize the reinforcement between users, communities and pages. Finally, we design several experiments to evaluate the performance of CAIS and compare it to other approaches. We prove that CAIS has a better performance for the user ranking of simulated data and real data from Del.ici.ous.
AB - Folksonomy systems provide a way for users to share and organize bookmarks. The social relationship among users has become stronger with the rapid development of new technologies. Finding the leading objects has become an important topic. These research topics are always centered around finding the most popular pages or experts. In this paper, we propose a new notion of expertise, which we call user insight. User insight denotes the user's expertise in finding Web pages that are useful or have the potential to be popular pages before other users find them. To address the issue, we refer to three major types of Web pages, namely, isolated, well-known, and burgeoning. Burgeoning pages are exceptionally useful and attractive for users in a folksonomy system. In our paper, we build a time-based algorithm to estimate user insight. In addition, we discuss the social relationship within fan networks, and we propose a link-based algorithm called CAIS (Community-based Annotation Insight Search) to realize the reinforcement between users, communities and pages. Finally, we design several experiments to evaluate the performance of CAIS and compare it to other approaches. We prove that CAIS has a better performance for the user ranking of simulated data and real data from Del.ici.ous.
UR - http://www.scopus.com/inward/record.url?scp=80052762406&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052762406&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2011.59
DO - 10.1109/ASONAM.2011.59
M3 - Conference contribution
AN - SCOPUS:80052762406
SN - 9780769543758
T3 - Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
SP - 353
EP - 360
BT - Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
Y2 - 25 July 2011 through 27 July 2011
ER -