Topic Exploration in Spatio-Temporal Document Collections

Zhao, Kaiqi; Chen, Lisi; Cong, Gao

doi:10.1145/2882903.2882921

Cited by 42 publications

(30 citation statements)

References 32 publications

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“…Other information associated with a location such as users' activities, and documents that induces spatio-temporal topics are also used for modeling user's mobility behavior [44,46]. These induced topics can be used to extend LoCaTe further, based on the availability of the integrated data (checkins along with location and social information).…”

Section: Related Workmentioning

confidence: 99%

Location-Specific Influence Quantification in Location-Based Social Networks

Likhyani

Bedathur

Deepak

2019

ACM Trans. Intell. Syst. Technol.

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Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other's physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a framework called LoCaTe, that combines (a) a user mobility model based on kernel density estimates; (b) a model of the semantics of the location using topic models; and (c) a user correlation model that uses an exponential distribution. We further develop LoCaTe+, an advanced model within the same framework where user correlation is quantified using a Mutually Exciting Hawkes Process. We show the applicability of LoCaTe and LoCaTe+ for location promotion and location recommendation tasks using LBSNs. Our models are validated using a long-term crawl of Foursquare data collected between Jan 2015-Feb 2016, as well as other publicly available LBSN datasets. Our experiments demonstrate the efficacy of the LoCaTe framework in capturing location-specific influence between users. We also show that our models improve over state-of-the-art models for the task of location promotion as well as location recommendation.

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Section: Related Workmentioning

confidence: 99%

Location-Specific Influence Quantification in Location-Based Social Networks

Likhyani

Bedathur

Deepak

2019

ACM Trans. Intell. Syst. Technol.

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“…Another study considers the problem of selectivity estimation [52] for a user-specified region. Based on precomputed probabilistic topic models for each grid cell, a recent study [66] proposes an approach to efficiently discover topics for a user-specified region. Additionally, event detection [1] is performed for a given region over text streams [51].…”

Section: Exploratory Search and Miningmentioning

confidence: 99%

Querying Geo-Textual Data

Cong

Jensen

2016

Proceedings of the 2016 International Conference on Management of Data

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Over the past decade, we have moved from a predominantly desktop based web to a predominantly mobile web, where users most often access the web from mobile devices such as smartphones. In addition, we are witnessing a proliferation of geo-located, textual web content. Motivated in part by these developments, the research community has been hard at work enabling the efficient computation of a variety of query functionality on geo-textual data, yielding a sizable body of literature on the querying of geo-textual data.With a focus on different types of keyword-based queries on geo-textual data, the tutorial also explores topics such as continuous queries on streaming geo-textual data, queries that retrieve attractive regions of geo-textual objects, and queries that extract properties, e.g., topics and top-k frequent words, of the objects in regions. The tutorial is designed to offer an overview of the problems addressed in this body of literature and offers an overview of pertinent concepts and techniques. In addition, the tutorial suggests open problems and new research direction.

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“…Computing the topic model for a topic-range query with efficiency becomes the goal of many research proposals. An algorithm named Fast Set Sampling algorithm (FSS) has been proposed for topic-range query [15]. The original FSS algorithm indexes the whole corpus with a tree, and pre-compute the topic models every node of the tree.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Therefore, all these leaf nodes are added to the query result dataset. Similar to 1D range query, for the first dimension, the query time complexity is O(logn), and the number of subtrees in the Canonical Set of [5,15] is in O(logn). Therefore, for the second dimension, there will be O(logn) subtrees being queried.…”

Section: Range Treementioning

confidence: 99%

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Topic Ranger : a tool for topic exploration and analysis of spatio-temporal documents

Lü¹

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Topic Exploration in Spatio-Temporal Document Collections

Cited by 42 publications

References 32 publications

Location-Specific Influence Quantification in Location-Based Social Networks

Location-Specific Influence Quantification in Location-Based Social Networks

Querying Geo-Textual Data

Topic Ranger : a tool for topic exploration and analysis of spatio-temporal documents

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