Collaborative recommendation for user spatial activities in mobile environments

Recommendation technologies are facing new challenges in mobile environments due to the complexity of user behaviors in dynamic contexts. In this paper, we focus on the integration of internet content access with user natural behaviors, and propose a context-aware collaborative recommendation paradigm for user spatial activities in mobile environments. In the proposed approach, potential user behavior patterns with contexts and preferences are discovered from historical logs. Then, temporal activity prediction and service recommendation tasks are performed according to the target user's real-time behavioral contexts using an improved collaborative filtering algorithm. Analysis and experiments indicate that our approach can effectively improve the quality of service recommendation in mobile computing environments.