Bidirectional sensing of user preferences and application changes for dynamic mobile app recommendations

Abstract

Recent years have witnessed the rapid adoption of mobile devices and significant growth in the use of mobile apps. However, the large number of mobile apps makes it difficult for users to determine which ones are of interest. Current app recommendation methods focus on the similarities between user interest and mobile app functionality at a single moment, but they do not consider how these change over time and do not address the underlying mutual effect of such changes. This paper describes a bidirectional sensing method for user interest and app changes that enables precise mobile app recommendations. The proposed method applies continuous bidirectional sensing to both user interest and the functional changes to apps and obtains the evolution trajectory of both. Then, a personal interest evolution network is used to learn the relationship between the two trajectories. Finally, user interest in an app is predicted, and the top-N strategy is used to recommend suitable apps. Experiments on real data sets show that our method can accurately sense user interests and functional changes to apps, and that recommendations based on the personal interest evolution network correspond to actual recommendation scenarios.