  Social networks include millions of users constantly looking for new
relationships for personal or professional purposes. Social network sites
recommend friends based on relationship features and content information. A
significant part of information shared every day is spread in Hashtags. None of
the existing content-based recommender systems uses the semantic of hashtags
while suggesting new friends. Currently, hashtags are considered as strings
without looking at their meanings. Social network sites group together people
sharing exactly the same hashtags and never semantically close ones. We think
that hashtags encapsulate some people interests. In this paper, we propose a
framework showing how a recommender system can benefit from hashtags to enrich
users' profiles. This framework consists of three main components: (1)
constructing user's profile based on shared hashtags, (2) matching method that
computes semantic similarity between profiles, (3) grouping semantically close
users using clustering technics. The proposed framework has been tested on a
Twitter dataset from the Stanford Large Network Dataset Collection consisting
of 81306 profiles.
