TY - GEN
T1 - Where did you go
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
AU - Wu, Fei
AU - Li, Zhenhui
N1 - Funding Information:
The work was supported in part by NSF award #1618448 and #1544455. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agencies.
Publisher Copyright:
© 2016 ACM.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - Recent advances in positioning technology have generated massive volume of human mobility data. At the same time, large amount of spatial context data are available and provide us with rich context information. Combining the mobility data with surrounding spatial context enables us to understand the semantics of the mobility records, e.g., what is a user doing at a location (e.g., dining at a restaurant or attending a football game). In this paper, we aim to answer this question by annotating the mobility records with surrounding venues that were actually visited by the user. The problem is non-trivial due to high ambiguity of surrounding contexts. Unlike existing methods that annotate each location record independently, we propose to use all historical mobility records to capture user preferences, which results in more accurate annotations. Our method does not assume the availability to any training data on user preference because of the difficulties to obtain such data in the real-world setting. Instead, we design a Markov random field model to find the best annotations that maximize the consistency of annotated venues. Through extensive experiments on real datasets, we demonstrate that our method significantly outperforms the baseline methods.
AB - Recent advances in positioning technology have generated massive volume of human mobility data. At the same time, large amount of spatial context data are available and provide us with rich context information. Combining the mobility data with surrounding spatial context enables us to understand the semantics of the mobility records, e.g., what is a user doing at a location (e.g., dining at a restaurant or attending a football game). In this paper, we aim to answer this question by annotating the mobility records with surrounding venues that were actually visited by the user. The problem is non-trivial due to high ambiguity of surrounding contexts. Unlike existing methods that annotate each location record independently, we propose to use all historical mobility records to capture user preferences, which results in more accurate annotations. Our method does not assume the availability to any training data on user preference because of the difficulties to obtain such data in the real-world setting. Instead, we design a Markov random field model to find the best annotations that maximize the consistency of annotated venues. Through extensive experiments on real datasets, we demonstrate that our method significantly outperforms the baseline methods.
UR - http://www.scopus.com/inward/record.url?scp=84996549843&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996549843&partnerID=8YFLogxK
U2 - 10.1145/2983323.2983845
DO - 10.1145/2983323.2983845
M3 - Conference contribution
AN - SCOPUS:84996549843
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 589
EP - 598
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 24 October 2016 through 28 October 2016
ER -