Abstract
POI recommendation is a significant service for LBSNs. It recommends new places such as clubs, restaurants, and coffee bars to users. Whether recommended locations meet users’ interests depends on three factors: user preference, social influence, and geographical influence. Especially, capturing the geographical influence plays the most important role for POI recommendations. Previous studies observe that checked-in locations disperse around several centers and employ Gaussian distribution based models to approximate users’ check-in behaviors. Yet centers discovering methods are not satisfactory in prior work. This chapter shows how to exploit Gaussian mixture model (GMM) and genetic algorithm based Gaussian mixture model (GA-GMM) to capture geographical influence. Experimental results on a real-world LBSN dataset show that GMM beats several popular geographical capturing models in terms of POI recommendation, while GA-GMM excludes the effect of outliers and enhances GMM.
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Zhao, S., Lyu, M.R., King, I. (2018). Understanding Human Mobility from Geographical Perspective. In: Point-of-Interest Recommendation in Location-Based Social Networks. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-1349-3_2
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DOI: https://doi.org/10.1007/978-981-13-1349-3_2
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