Abstract
People often share their visited Points-of-Interest (PoIs) by “check-ins”. On the one hand, human mobility varies with each individual but still implies regularity. Check-ins of an individual tend to localize in a specific geographical range. We propose a novel model to capture personalized geographical constraint of each individual. On the other hand, PoIs reflect requirements of people from different aspects. Usually, places of different functions show different temporal visiting distributions and places of similar function share similar visiting pattern in temporal aspect. Temporal distribution similarity can be used to characterize functional similarity. Based on the findings above, this paper introduces improved collaborative filtering models by jointly taking advantages of geographical constraint and temporal similarity. Experimental results on real data collected from Gowalla and JiePang demonstrate the effectiveness of our models.
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Wu, H., Shao, J., Yin, H., Shen, H.T., Zhou, X. (2015). Geographical Constraint and Temporal Similarity Modeling for Point-of-Interest Recommendation. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_40
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DOI: https://doi.org/10.1007/978-3-319-26187-4_40
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