  With the rapid development of mobile apps, the availability of a large number
of mobile apps in application stores brings challenge to locate appropriate
apps for users. Providing accurate mobile app recommendation for users becomes
an imperative task. Conventional approaches mainly focus on learning users'
preferences and app features to predict the user-app ratings. However, most of
them did not consider the interactions among the context information of apps.
To address this issue, we propose a broad learning approach for
\textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor
\textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to
effectively integrate user's preference, app category information and
multi-view features to facilitate the performance of app rating prediction. The
multidimensional structure is employed to capture the hidden relationships
between multiple app categories with multi-view features. We develop an
efficient factorization method which applies Tucker decomposition to learn the
full-order interactions within multiple categories and features. Furthermore,
we employ a group $\ell_{1}-$norm regularization to learn the group-wise
feature importance of each view with respect to each app category. Experiments
on two real-world mobile app datasets demonstrate the effectiveness of the
proposed method.
