Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011 |
| Pages | 353-360 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2011 Sep 19 |
| Event | 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011 - Kaohsiung, Taiwan Duration: 2011 Jul 25 → 2011 Jul 27 |
Publication series
| Name | Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011 |
|---|
Other
| Other | 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011 |
|---|---|
| Country | Taiwan |
| City | Kaohsiung |
| Period | 11-07-25 → 11-07-27 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Electrical and Electronic Engineering
Cite this
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CAIS : Community based annotation insight search in a folksonomy network. / Huang, Han Chang; Kao, Hung-Yu.
Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011. 2011. p. 353-360 5992599 (Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - CAIS
T2 - Community based annotation insight search in a folksonomy network
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
ER -