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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Context Recovery In Location-Based Social Networks, Wen Haw Chong
Context Recovery In Location-Based Social Networks, Wen Haw Chong
Dissertations and Theses Collection (Open Access)
This dissertation addresses context recovery in Location-Based Social Networks (LBSN), which are platforms where users post content from various locations. With this general LBSN definition, many existing social media platforms that support user-generated location relevant content using mobile devices could also qualify as LBSNs. Context recovery for such user posts refers to recovering the venue and the semantic contexts of these user posts. Such information is useful for user profiling and to support various applications such as venue recommendation and location- based advertising.
Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh Nam Doan, Ee-Peng Lim
Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh Nam Doan, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as well …