TY - GEN
T1 - Modeling user expertise in folksonomies by fusing multi-type features
AU - Yao, Junjie
AU - Cui, Bin
AU - Han, Qiaosha
AU - Zhang, Ce
AU - Zhou, Yanhong
PY - 2011
Y1 - 2011
N2 - The folksonomy refers to the online collaborative tagging system which offers a new open platform for content annotation with uncontrolled vocabulary. As folksonomies are gaining in popularity, the expert search and spammer detection in folksonomies attract more and more attention. However, most of previous work are limited on some folksonomy features. In this paper, we introduce a generic and flexible user expertise model for expert search and spammer detection. We first investigate a comprehensive set of expertise evidences related to users, objects and tags in folksonomies. Then we discuss the rich interactions between them and propose a unified Continuous CRF model to integrate these features and interactions. This model's applications for expert recommendation and spammer detection are also exploited. Extensive experiments are conducted on a real tagging dataset and demonstrate the model's advantages over previous methods, both in performance and coverage.
AB - The folksonomy refers to the online collaborative tagging system which offers a new open platform for content annotation with uncontrolled vocabulary. As folksonomies are gaining in popularity, the expert search and spammer detection in folksonomies attract more and more attention. However, most of previous work are limited on some folksonomy features. In this paper, we introduce a generic and flexible user expertise model for expert search and spammer detection. We first investigate a comprehensive set of expertise evidences related to users, objects and tags in folksonomies. Then we discuss the rich interactions between them and propose a unified Continuous CRF model to integrate these features and interactions. This model's applications for expert recommendation and spammer detection are also exploited. Extensive experiments are conducted on a real tagging dataset and demonstrate the model's advantages over previous methods, both in performance and coverage.
UR - https://www.scopus.com/pages/publications/79955157052
U2 - 10.1007/978-3-642-20149-3_6
DO - 10.1007/978-3-642-20149-3_6
M3 - 会议稿件
AN - SCOPUS:79955157052
SN - 9783642201486
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 67
BT - Database Systems for Advanced Applications - 16th International Conference, DASFAA 2011, Proceedings
T2 - 16th International Conference on Database Systems for Advanced Applications, DASFAA 2011
Y2 - 22 April 2011 through 25 April 2011
ER -