CAMAR: a broad learning based context-aware recommender for mobile applications

The emergence of a large number of mobile apps brings challenges to locate appropriate apps for users, which makes mobile app recommendation an imperative task. In this paper, we first conduct detailed data analysis to show the characteristics of mobile apps which are different with conventional items (e.g., movies, books). Considering the specific property of mobile apps, we propose a broad learning approach for context-aware mobile app recommendation with tensor analysis (CAMAR). Specifically, we utilize a tensor-based framework to effectively integrate app category information and multi-view features on users and apps to facilitate the performance of app recommendation. The multi-dimensional structure is employed to capture the hidden relationships among the app categories and multi-view features. We develop an efficient factorization method which applies Tucker decomposition to jointly learn the full-order interactions among the app categories and features without physically building the tensor. Furthermore, we employ a groupl1 norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.