Conference Proceedings


Andy Way
Mohammed Hasanuzzaman


Computer Science

feature extraction experimental study machine translating public private multi objective location based social networks poi recommendation place type tagging

Place-type detection in location-based social networks (2017)

Abstract 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 offering a wide range of possible applications. Based on the check-in records, we compute two types of place features from (i) specific patterns of individual places and (ii) latent relatedness among similar places. ´┐Że features extracted from specific patterns (SP) are derived from all check-ins at a specific place. The 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 Twitter. Experimental results demonstrate the effectiveness 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 beneficial for place name recommendation services.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Andy Way, Mohammed Hasanuzzaman

Experts in our system

Andy Way
Dublin City University
Total Publications: 229
Mohammed Hasanuzzaman
Dublin City University
Total Publications: 9