SMART: Sponsored Mobile App RecommendaTion by Balancing App Downloads and Appstore Profit

摘要

With the explosive growth of smartphone market, mobile applications (short as apps) have recently gained great attention. One mature business paradigm nowadays is that apps can be financially sponsored and appstores can benefit from the distribution of these apps. A good mobile app recommender system should be able to pursue such sponsored profit while maintaining the recommendation quality. We name this scenario as Sponsored Mobile App RecommendaTion (SMART), a research topic that has not been fully explored before. To solve this problem, we propose a Similar App Substitution (SAS) principle, stating that among apps with similar properties we can safely select those with high profits. Guided by SAS, we propose a Profit-regularized Kernel Least Square (PKLS) algorithm. In PKLS, multi-kernel representation is applied to capture app properties, the Profit-Per-Download (PPD) of apps serves as regularization, and we design a dynamic learning strategy to update parameters based on user feedbacks. Extensive experiments are conducted with both offline simulation and online deployment on a well-known appstore in China. The results show that our PKLS algorithm achieves better balance between app downloads and appstore profit than the comparison algorithms.