As a tremendous number of mobile applications (apps) are readily
available, users have difﬁculty in identifying apps that are relevant
to their interests. Recommender systems that depend on previous user ratings (i.e., collaborative ﬁltering, or CF) can address this problem for apps that have sufﬁcient ratings from past users. But
for apps that are newly released, CF does not have any user ratings to base recommendations on, which leads to the cold-start problem.
In this paper, we describe a method that accounts for nascent information culled from Twitter to provide relevant recommendation in such cold-start situations. We use Twitter handles to access an app’s Twitter account and extract the IDs of their Twitter-followers.
We create pseudo-documents that contain the IDs of Twitter users interested in an app and then apply latent Dirichlet allocation to
generate latent groups. At test time, a target user seeking recommendations is mapped to these latent groups. By using the transitive relationship of latent groups to apps, we estimate the probability of the user liking the app. We show that by incorporating
information from Twitter, our approach overcomes the difﬁculty of
cold-start app recommendation and signiﬁcantly outperforms other
state-of-the-art recommendation techniques by up to 33%.