TY - GEN
T1 - On the semantic annotation of places in location-based social networks
AU - Ye, Mao
AU - Shou, Dong
AU - Lee, Wang Chien
AU - Yin, Peifeng
AU - Janowicz, Krzysztof
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80052649964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052649964&partnerID=8YFLogxK
U2 - 10.1145/2020408.2020491
DO - 10.1145/2020408.2020491
M3 - Conference contribution
AN - SCOPUS:80052649964
SN - 9781450308137
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 520
EP - 528
BT - Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
PB - Association for Computing Machinery
T2 - 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011
Y2 - 21 August 2011 through 24 August 2011
ER -