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.
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Acknowledgment
This work was partially supported by the National Natural Science Foundation of China under Grant (Nos. U1811261, U1435216, and 61602103).
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He, X., Zhang, T., Liu, H., Yu, G. (2020). An ELM-Based Ensemble Strategy for POI Recommendation. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_32
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DOI: https://doi.org/10.1007/978-3-030-23307-5_32
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