Article,
UserRec: A User Recommendation Framework in Social Tagging Systems
(2010)
Abstract
Social tagging systems have emerged as an effective way for
users to annotate and share objects on the Web. However,
with the growth of social tagging systems, users are easily
overwhelmed by the large amount of data and it is very difficult
for users to dig out information that he/she is interested
in. Though the tagging system has provided interestbased
social network features to enable the user to keep track
of other users’ tagging activities, there is still no automatic
and effective way for the user to discover other users with
common interests. In this paper, we propose a User Recommendation
(UserRec) framework for user interest modeling
and interest-based user recommendation, aiming to boost information
sharing among users with similar interests. Our
work brings three major contributions to the research community:
(1) we propose a tag-graph based community detection
method to model the users’ personal interests, which
are further represented by discrete topic distributions; (2)
the similarity values between users’ topic distributions are
measured by Kullback-Leibler divergence (KL-divergence),
and the similarity values are further used to perform interestbased
user recommendation; and (3) by analyzing users’
roles in a tagging system, we find users’ roles in a tagging
system are similar to Web pages in the Internet. Experiments
on tagging dataset of Web pages (Yahoo! Delicious) show
that UserRec outperforms other state-of-the-art recommender
system approaches.
