Inproceedings,
SemRec: A Semantic Enhancement Framework for Tag Based Recommendation.
AAAI, AAAI Press, (2011)
Abstract
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 nature,
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 explicit
structural information among users, resources and tags
into consideration, while neglecting the important implicit semantic
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 experiments
conducted on two real datasets demonstrate the effectiveness
of our approaches
