A Broad Learning Approach for Context-Aware Mobile Application Recommendation

• Main Authors: Tingting Liang, Lifang He, Chun-Ta Lu, Liang Chen, Yu, Philip S., Jian Wu
• Format: Conference Proceeding
• Language: English
• Subjects: Buildings; Computer science; Mobile applications; Mobile communication; Navigation; Tensile stress
• ISSN: 2374-8486
• DOI: 10.1109/ICDM.2017.121

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenges 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 Context-Aware app recommendation with Tensor Analysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate app category information and multi-view features on users and apps, respectively, to facilitate the performance of rating prediction. The multidimensional structure is employed to capture the hidden relationships among the app categories and the multiview features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions among the app categories and features. Furthermore, we employ a group ℓ 1 -norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on a real-world mobile app dataset demonstrate the effectiveness of the proposed method.