NDLI: Mining user check-in features for location classification in location-based social networks

Content Provider	IEEE Xplore Digital Library
Author	Yu, Chen Liu, Yang Yao, Dezhong Jin, Hai Lu, Feng Chen, Hanhua Ding, Qiang
Copyright Year	2015
Description	Author affiliation: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China (Yu, Chen; Liu, Yang; Yao, Dezhong; Jin, Hai; Lu, Feng; Chen, Hanhua) \|\| Shannon Lab, Huawei Technologies Co., LTD, Beijing, 100085, China (Ding, Qiang)
Abstract	With the increasing popularity of location-based social networks, a large number of users have been involved in the check-ins. The venues where the user frequently repeats check-ins tend to play a very important role in his daily life, as they not only dominate the user's mobility behavior but also imply the user's personal preferences. Therefore, fast discerning of such check-in venues could enable us to improve a wide range of location-based services. In this paper, we propose a new location classification problem for users of location-based social networks, in which we aim to discern, given the observation that a user makes a "new" check-in at a venue, whether he will frequently repeat check-ins at this venue. To solve the problem, we first extract 16 features attached to the user's "new" check-ins. With the publicly available check-in dataset, we then train a location classifier based on Support Vector Machine and compare it with two baselines based on majority voting. The comparison results demonstrate the practicability of the trained location classifier.
Starting Page	385
Ending Page	390
File Size	192987
Page Count	6
File Format	PDF
e-ISBN	9781467371940
DOI	10.1109/ISCC.2015.7405545
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Social network services Frequency measurement Computers Support vector machines Global Positioning System Mobile radio mobility management location classification location-based social networks data mining
Content Type	Text
Resource Type	Article

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