  Twitter, one of the biggest and most popular microblogging Websites, has
evolved into a powerful communication platform which allows millions of active
users to generate huge volume of microposts and queries on a daily basis. To
accommodate effective categorization and easy search, users are allowed to make
use of hashtags, keywords or phrases prefixed by hash character, to categorize
and summarize their posts. However, valid hashtags are not restricted and thus
are created in a free and heterogeneous style, increasing difficulty of the
task of tweet categorization. In this paper, we propose a low-rank weighted
matrix factorization based method to recommend hashtags to the users solely
based on their hashtag usage history and independent from their tweets'
contents. We confirm using two-sample t-test that users are more likely to
adopt new hashtags similar to the ones they have previously adopted. In
particular, we formulate the problem of hashtag recommendation into an
optimization problem and incorporate hashtag correlation weight matrix into it
to account for the similarity between different hashtags. We finally leverage
widely used matrix factorization from recommender systems to solve the
optimization problem by capturing the latent factors of users and hashtags.
Empirical experiments demonstrate that our method is capable to properly
recommend hashtags.
