Proceedings ArticleDOI
On the semantic annotation of places in location-based social networks
Mao Ye,Dong Shou,Wang-Chien Lee,Peifeng Yin,Krzysztof Janowicz +4 more
- pp 520-528
TLDR
A semantic annotation technique for location-based social networks to automatically annotate all places with category tags which are a crucial prerequisite for location search, recommendation services, or data cleaning is developed.Abstract:
In this paper, we develop a semantic annotation technique for location-based social networks to automatically annotate all places with category tags which are a crucial prerequisite for location search, recommendation services, or data cleaning. Our annotation algorithm learns a binary support vector machine (SVM) classifier for each tag in the tag space to support multi-label classification. Based on the check-in behavior of users, we extract features of places from i) explicit patterns (EP) of individual places and ii) implicit relatedness (IR) among similar places. The features extracted from EP are summarized from all check-ins at a specific place. The features from IR are derived by building a novel network of related places (NRP) where similar places are linked by virtual edges. Upon NRP, we determine the probability of a category tag for each place by exploring the relatedness of places. Finally, we conduct a comprehensive experimental study based on a real dataset collected from a location-based social network, Whrrl. The results demonstrate the suitability of our approach and show the strength of taking both EP and IR into account in feature extraction.read more
Citations
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Proceedings ArticleDOI
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation
TL;DR: The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon in human mobility behavior on the LBSNs into matrixfactorization improves recommendation performance.
BookDOI
Computing with Spatial Trajectories
Yu Zheng,Xiaofang Zhou +1 more
TL;DR: This book presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks.
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Recommendations in location-based social networks: a survey
TL;DR: A panorama of the recommender systems in location-based social networks with a balanced depth is presented, facilitating research into this important research theme.
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Inferring anchor links across multiple heterogeneous social networks
TL;DR: This paper proposes to extract heterogeneous features from multiple heterogeneous networks for anchor link prediction, including user's social, spatial, temporal and text information, and derives an effective solution, MNA (Multi-Network Anchoring), to infer anchor links w.r.t. the one-to-one constraint.
Proceedings ArticleDOI
Learning Graph-based POI Embedding for Location-based Recommendation
TL;DR: A generic graph-based embedding model is proposed that jointly captures the sequential effect, geographical influence, temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs into a shared low dimensional space and develops a novel time-decay method to dynamically compute the user's latest preferences.
References
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