Exploring trajectory-driven local geographic topics in foursquare

Long, Xuelian; Jin, Lei; Joshi, James

doi:10.1145/2370216.2370423

Cited by 64 publications

(34 citation statements)

References 12 publications

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“…Second, telecommunications or Information and Communications Technology (ICT) data also evidence social relationships as sourced from e-mails (e.g., Tyler et al 2005), SMS, websites, or instant message (IM) services (e.g., Leskovec and Horvitz 2008); geotagged photo uploads (e.g., Crandall et al 2010) online check-in sites (e.g., Long et al 2012), postal mail (e.g., Milgram 1967), point-to-point landline or mobile phone calls (e.g., Eagle et al 2010), or IP address hits to certain geolocated websites.…”

Section: Approachmentioning

confidence: 99%

Integrating social network data into GISystems

Andris

2016

International Journal of Geographical Information Science

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Today, online social media outlets provide new and plentiful sources of data on social networks (SNs) and location-based social networks (LBSNs), i.e., geolocated evidence of connections between individuals. While SNs have been used to show how the magnitude of social connectivity decreases with distance, there are few examples of how to include SNs as layers in a GISystem. If SNs, and thus, interpersonal relationships, could be analyzed in a geographic information system (GIS) setting, we could better model how humans socialize, share information, and form social groups within the complex geographic landscape.Our goal is to facilitate a guide for analyzing SNs (as derived from online social media, telecommunications, surveys, etc.) within geographic space by combining the mature fields of social network analysis (SNA) and GISystems. First, we describe why modeling socialization in geographic space is essential for understanding human behavior. We then outline best practices and techniques for embedding SN nodes and edges in GISystems by introducing terms like 'social flow' and 'anthrospace', and categorizations for data and spatial aggregation types. Finally, we explore case study vignettes of SNA within GISystems from diverse regions located in Bolivia, China, Côte d'Ivoire, Singapore, the United Kingdom, and the United States, using concepts such as geolocated dyads, egoalter relationships, node feature roles, modularity, and network transitivity. ARTICLE HISTORY

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Section: Approachmentioning

confidence: 99%

Integrating social network data into GISystems

Andris

2016

International Journal of Geographical Information Science

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“…Since then, topic models have been widely applied in various domains, including social media platforms. Recent works [4,5] had applied LDA on Foursquare check-ins. Both works model the users as high level documents containing venues as terms.…”

Section: Proposed Research Ideamentioning

confidence: 99%

“…Currently Foursquare 1 reports a user base of over 50 million, with more than 6 billion check-ins generated. Thus it is not surprising that check-ins has been especially well studied for user profiling and modeling [2,4,5,7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Venues in Foursquare Using Flipped Topic Models

Chong

Dai

Lim

2015

Lecture Notes in Computer Science

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Abstract. Foursquare is a highly popular location-based social platform, where users indicate their presence at venues via check-ins and/or provide venue-related tips. On Foursquare, we explore Latent Dirichlet Allocation (LDA) topic models for venue prediction: predict venues that a user is likely to visit, given his history of other visited venues. However we depart from prior works which regard the users as documents and their visited venues as terms. Instead we 'flip' LDA models such that we regard venues as documents that attract users, which are now the terms. Flipping is simple and requires no changes to the LDA mechanism. Yet it improves prediction accuracy significantly as shown in our experiments. Furthermore, flipped models are superior when we model tips and checkins as separate modes. This enables us to use tips to improve prediction accuracy, which is previously unexplored. Lastly, we observed the largest accuracy improvement for venues with fewer visitors, implying that the flipped models cope with sparse venue data more effectively.

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“…Long et al also take advantage of LDA model to discover the local geographic topic form the check-in record extracted from Foursquare [12]. Van Setten et al adopt a blended model.…”

Section: Related Workmentioning

confidence: 99%

Exploring Personalized Travel Route Using POIs

Li¹,

Yang²,

Liu³

2015

IJCTE

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Abstract-The development of travel industry gradually fosters personalized requirements of tourists, such as setting the start place, exploiting interesting activities and organizing the travel route. In this paper, we take advantage of two online websites to collect information of both scenic spots and real-world local activities, and we proposed three rule-based methods to recommend travel routes for tourists, which can take their instant location into account and satisfy their personalized demands. Specifically, we extract scenery spots dataset from Flickr and popular activities from Douban-Event in line with the visiting city and date. Then we process these items and take the filtering results as the recommendation candidates. Finally based on popularity and geographical location of these venues/activities, we focus on arranging a colorful travel route, which covers not only the everlasting scenic spots, but also some temporary activities. We evaluate the quality of travel routes produced by our recommendation approaches with an alternative plan suggested by a professional travel site. The results justify the advantages of our approaches over the baseline method provided by travel experts.

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Exploring trajectory-driven local geographic topics in foursquare

Cited by 64 publications

References 12 publications

Integrating social network data into GISystems

Integrating social network data into GISystems

Prediction of Venues in Foursquare Using Flipped Topic Models

Exploring Personalized Travel Route Using POIs

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