A Thematic Approach to User Similarity Built on Geosocial Check-ins

McKenzie, Grant; Adams, Benjamin; Janowicz, Krzysztof

doi:10.1007/978-3-319-00615-4_3

Cited by 15 publications

(11 citation statements)

References 16 publications

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“…User annotations in the form of tips and comments are analysed collectively to extract general topics to characterise places or to extract collective sentiment indications about the place. Examples of works that considered place categories are [14][15][16][17]. In [14,15], the latent Dirichlet allocation (LDA) model was used to represent places as a probability distribution over topics collected from tags and categories or comments made in a place and, similarly, aggregate all tips from places a user has visited to model a user's interest.…”

Section: Related Workmentioning

confidence: 99%

“…Examples of works that considered place categories are [14][15][16][17]. In [14,15], the latent Dirichlet allocation (LDA) model was used to represent places as a probability distribution over topics collected from tags and categories or comments made in a place and, similarly, aggregate all tips from places a user has visited to model a user's interest. Aggregation was necessary, as terms associated with a single POI are usually short, incomplete, and ambiguous.…”

Section: Related Workmentioning

confidence: 99%

“…While the authors made use of check-in information, they relied on the hierarchy of location categories supplied by Foursquare in conjunction with the frequency of check-ins to determine a measure of similarity. Mckenzie et al [15] suggest exploring unstructured user-contributed data, namely tips provided by users. A topic modelling approach is used to represent users' interests in places.…”

Section: Related Workmentioning

confidence: 99%

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Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

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Knowledge of users' visits to places is one of the keys to understanding their interest in places. Usercontributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users' association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

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“…Recent advancements in semantic analysis [Joachims, 1998] and probabilistic topic models [Blei et al, 2003;Ramage et al, 2009] have made it feasible to infer and measure similarities between documents. These topic-based approaches have emerged in the geospatial science literature as well with researchers geolocating individuals based on the content of their social contributions [Cheng et al, 2013;Hecht et al, 2011;Li et al, 2008] and building location recommendation systems [Bao et al, 2012;Matyas and Schlieder, 2009;McKenzie et al, 2013a], to name a few. It has been shown in previous work that individual words and topics in place descriptions are indicative of geospatial location [Adams and Janowicz, 2012].…”

Section: Related Workmentioning

confidence: 99%

“…For example, this would enable queries for places visited by friends, that have a low noise level, friendly staff, and free wifi. Our more immediate interest, however, lies in exploiting the conflated POI to improve user similarity measures for the analysis of sparse semantic trajectories [McKenzie et al, 2013a].…”

Section: Introductionmentioning

confidence: 99%

A weighted multi-attribute method for matching user-generated Points of Interest

McKenzie

Janowicz

Adams

2014

Cartography and Geographic Information Science

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To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multidimensional data sources and not merely its volume. To fully exploit this variety, however, requires conflation. This is a two step process. First, one has to establish identity relations between information entities across the different data sources; and second, attribute values have to be merged according to certain procedures which avoid logical contradictions. The first step, also called matching, can be thought of as a weighted combination of common attributes according to some similarity measures. In this work, we propose such a matching based on multiple attributes of Points of Interests (POI) from the Location-based Social Network Foursquare and the Yelp local directory service. While both contain overlapping attributes that can be use for matching, they have specific strengths and weaknesses which makes their conflation desirable. For instance, Foursquare offers information about user check-ins to places, while Yelp specializes in user-contributed reviews. We present a weighted multi-attribute matching strategy, evaluate its performance, and discuss application areas that benefit from a successful matching. Finally, we also outline how the established POI matches can be stored as Linked Data on the Semantic Web. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities.

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A Geo-ontology Design Pattern for Semantic Trajectories

Janowicz

Carral

et al. 2013

Lecture Notes in Computer Science

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Abstract. Trajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.

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A Thematic Approach to User Similarity Built on Geosocial Check-ins

Cited by 15 publications

References 16 publications

Spatio-semantic user profiles in location-based social networks

Spatio-semantic user profiles in location-based social networks

A weighted multi-attribute method for matching user-generated Points of Interest

A Geo-ontology Design Pattern for Semantic Trajectories

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