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Abstract

Twitter and Foursquare are two well-connected platforms for sharing information where growing numbers of users post location-related messages. In contrast to the longitude-latitude geotags commonly used online, e.g., on photos and tweets, new place-tags containing category information show more human-readable high-level information rather than a pair of coordinates. This grants an opportunity for better understanding users' physical locations which can be used as context to facilitate other applications, e.g., location context-aware advertisement. In this paper, we verify the assumption that users' current trails contain cues of their future routes. The results from the preliminary experiments show promising performance of a basic Markov Chain-based model.

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.