Collaborative recommendation of mobile apps: A swarm intelligence method

Abstract

The explosive growth of mobile apps has given rise to the challenge of finding out interesting apps for users. Recommender systems are employed to meet this challenge. However, as the lack of user and app data, the development of recommender systems for mobile apps is still at a slow pace. Therefore, we propose a system-level collaboration approach to facilitate the development of new systems by making a better use of the data from existing systems. To this end, we model the recommendation generation as an optimization problem and propose a new set-based particle swarm optimization method to solve it. We further develop three systems to evaluate our approach and algorithm. Evaluations based on real data have verified their performances on both the effectiveness and the efficiency. © Springer-Verlag Berlin Heidelberg 2014.