Abstract
With the surge of location-based tracking in social media, check-in recommendation has become the topic of interest in recent past. Technological advancements and sensor-embedded smartphones have made geospatial data to be easily shared. Using this data, spatiotemporal footprints can be obtained which guides us about digital trajectories both in space as well as time. In this paper, we have presented a framework for enhancing Geo-targeted and timely recommendations considering user location, time of visit and nearby place density. Our proposed system uses predictive models to train check-in data provided by Facebook over the Kaggle Website. Furthermore, we ensemble the outcome obtained from individual models to demonstrate the effectiveness of our approach to a certain extent.
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Solanki, D., Singh, R. (2020). Probabilistic Framework for Location Prediction Based on Temporal Density. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_34
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DOI: https://doi.org/10.1007/978-981-15-1420-3_34
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