  Advances in smartphone technology have promoted the rapid development of
mobile apps. However, the availability of a huge number of mobile apps in
application stores has imposed the challenge of finding the right apps to meet
the user needs. Indeed, there is a critical demand for personalized app
recommendations. Along this line, there are opportunities and challenges posed
by two unique characteristics of mobile apps. First, app markets have organized
apps in a hierarchical taxonomy. Second, apps with similar functionalities are
competing with each other. While there are a variety of approaches for mobile
app recommendations, these approaches do not have a focus on dealing with these
opportunities and challenges. To this end, in this paper, we provide a
systematic study for addressing these challenges. Specifically, we develop a
Structural User Choice Model (SUCM) to learn fine-grained user preferences by
exploiting the hierarchical taxonomy of apps as well as the competitive
relationships among apps. Moreover, we design an efficient learning algorithm
to estimate the parameters for the SUCM model. Finally, we perform extensive
experiments on a large app adoption data set collected from Google Play. The
results show that SUCM consistently outperforms state-of-the-art top-N
recommendation methods by a significant margin.
