Collaborative tagging services provided by various social
web sites become popular means to mark web resources for
different purposes such as categorization, expression of a
preference and so on. However, the tags are of syntactic na-
ture, in a free style and do not reflect semantics, resulting in
the problems of redundancy, ambiguity and less semantics.
Current tag-based recommender systems mainly take the ex-
plicit structural information
among users, resources and tags
into consideration, while neglec
ting the important implicit se-
mantic relationships hidden in tagging data. In this study, we
propose a
Semantic Enhancement Recommendation
strategy
(SemRec), based on both structural information and semantic
information through a unified fusion model. Extensive exper-
iments conducted on two real datasets demonstrate the effectiveness of our approaches.