An ELM-Based Ensemble Strategy for POI Recommendation

Abstract

The prosperity of Location-Based Social Networks (LBSNs) facilitates a promising focus on personalized POI recommendation. The check-in activity is a multiple criteria decision making process. In this paper, we devise a novel personalized POI recommendation system called Multiple Criteria Ensemble Recommendation System (MCERS) to integrate the effect of geographical influence, temporal influence and categorical influence in a unified way to model dynamic user preferences for context-aware query. Specifically, we propose an ELM-based ensemble strategy to provide a more sophisticated framework for integrating different criteria to improve the quality of POI recommendations. Extensive experiments on three real-world datasets show that MCERS achieves better performance than existing state-of-the-art methods.