Modeling User Expertise in Folksonomies by Fusing Multi-type Features

Abstract. 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