  We tackle the blog recommendation problem in Tumblr for mobile users in this
paper. Blog recommendation is challenging since most mobile users would suffer
from the cold start when there are only a limited number of blogs followed by
the user. Specifically to address this problem in the mobile domain, we take
into account mobile apps, which typically provide rich information from the
users. Based on the assumption that the user interests can be reflected from
their app usage patterns, we propose to exploit the app usage data for
improving blog recommendation. Building on the state-of-the-art recommendation
framework, Factorization Machines (FM), we implement app-based FM that
integrates app usage data with the user-blog follow relations. In this approach
the blog recommendation is generated not only based on the blogs that the user
followed before, but also the apps that the user has often used. We demonstrate
in a series of experiments that app-based FM can outperform other alternative
approaches to a significant extent. Our experimental results also show that
exploiting app usage information is particularly effective for improving blog
recommendation quality for cold start users.
