Conference Publication Details
Mandatory Fields
Mohammed Hasanuzzaman and Andy Way
HT-17 - 28th ACM Conference on Hypertext and Social Media
Place-Type Detection in Location-Based Social Networks
2017
July
Published
0
1 ()
Optional Fields
HT-17: Proceedings of the 28th ACM Conference on Hypertext and Social Media
75
83
Prague, Czech Republic
04-JUL-17
07-JUL-17
While most prior studies in Location-Based Social Networks (LSBNs) have mainly centered around areas such as Point-of-Interest (POI) recommendation and place tag annotation, there exists no works looking at the problem of associating place-type to venues in LBSNs. Determining the type of places in location-based social networks may contribute to the success of various downstream tasks such as Point-of-Interest recommendation, location search, automatic place name database creation, and data cleaning. In this paper, we propose a multi-objective ensemble learning framework that (i) allows the accurate tagging of places into one of the three categories: public, private, or virtual, and (ii) identifying a set of solutions thus o‚ering a wide range of possible applications. Based on the check-in records, we compute two types of place features from (i) speci€c paŠerns of individual places and (ii) latent relatedness among similar places. Œe features extracted from speci€c paŠerns (SP) are derived from all check-ins at a speci€c place. Œe features from latent relatedness (LR) are computed by building a graph of related places where similar types of places are connected by virtual edges. We conduct an experimental study based on a dataset of over 2.7M check-in records collected by crawling Foursquare-tagged tweets from TwiŠer. Experimental results demonstrate the e‚ectiveness of our approach to this new problem and show the strength of taking various methods into account in feature extraction. Moreover, we demonstrate how place type tagging can be bene€cial for place name recommendation services.
http://delivery.acm.org/10.1145/3080000/3078722/p75-hasanuzzaman.pdf?ip=136.206.217.57&id=3078722&acc=ACTIVE%20SERVICE&key=846C3111CE4A4710%2E821500BF45340188%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1522854770_25f2f5c459a823de5b822a9b965148cf
10.1145/3078714.3078722
Grant Details
Science Foundation Ireland (SFI)
13/RC/2106