Proceedings ArticleDOI
What you are is when you are: the temporal dimension of feature types in location-based social networks
Mao Ye,Krzysztof Janowicz,Christoph Mülligann,Wang-Chien Lee +3 more
- pp 102-111
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TLDR
This work extracts user check-ins from massive real-world data crawled from Location-based Social Networks to understand the temporal dimension of Points Of Interest.Abstract:
Feature types play a crucial role in understanding and analyzing geographic information. Usually, these types are defined, standardized, and controlled by domain experts and cover geographic features on the mesoscale level, e.g., populated places, forests, or lakes. While feature types also underlie most Location-Based Services (LBS), assigning a consistent typing schema for Points Of Interest (POI) across different data sets is challenging. In case of Volunteered Geographic Information (VGI), types are assigned as tags by a heterogeneous community with different backgrounds and applications in mind. Consequently, VGI research is shifting away from data completeness and positional accuracy as quality measures towards attribute accuracy. As tags can be assigned by everybody and have no formal or stable definition, we propose to study category tags via indirect observations. We extract user check-ins from massive real-world data crawled from Location-based Social Networks to understand the temporal dimension of Points Of Interest. While users may assign different category tags to places, we argue that their temporal characteristics, e.g., opening times, will show distinguishable patterns.read more
Citations
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Time-aware point-of-interest recommendation
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Recommendations in location-based social networks: a survey
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Proceedings ArticleDOI
Exploring temporal effects for location recommendation on location-based social networks
TL;DR: A novel location recommendation framework is introduced, based on the temporal properties of user movement observed from a real-world LBSN dataset, which exhibits the significance of temporal patterns in explaining user behavior, and demonstrates their power to improve location recommendation performance.
References
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