Constructing travel itineraries from tagged geo-temporal breadcrumbs

Choudhury, Munmun De; Feldman, Moran; Amer-Yahia, Sihem; Golbandi, Nadav; Lempel, Ronny; Yu, Cong

doi:10.1145/1772690.1772815

Cited by 45 publications

(23 citation statements)

References 6 publications

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“…Extra information can be incorporated within social media, such as geographical information captured by GPS devices. Studying such social media attracts both academic and industrial interests, and it has been a hot research area in recent years [31,11,30,29,3,9,27].…”

Section: Introductionmentioning

confidence: 99%

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Diversified Trajectory Pattern Ranking in Geo-Tagged Social Media

Yin¹,

Cao²,

Han³

et al. 2011

Proceedings of the 2011 SIAM International Conference on Data Mining

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Social media such as those residing in the popular photo sharing websites is attracting increasing attention in recent years. As a type of user-generated data, wisdom of the crowd is embedded inside such social media. In particular, millions of users upload to Flickr their photos, many associated with temporal and geographical information. In this paper, we investigate how to rank the trajectory patterns mined from the uploaded photos with geotags and timestamps. The main objective is to reveal the collective wisdom recorded in the seemingly isolated photos and the individual travel sequences reflected by the geo-tagged photos. Instead of focusing on mining frequent trajectory patterns from geo-tagged social media, we put more effort into ranking the mined trajectory patterns and diversifying the ranking results. Through leveraging the relationships among users, locations and trajectories, we rank the trajectory patterns. We then use an exemplar-based algorithm to diversify the results in order to discover the representative trajectory patterns. We have evaluated the proposed framework on 12 different cities using a Flickr dataset and demonstrated its effectiveness.

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Section: Introductionmentioning

confidence: 99%

“…There are some studies on trip planning using Flickr. In [9,8], Choudhury et al formulated trip planning as directed orienteering problem. In [27], Lu et al used dynamic programming for trip planning.…”

Section: Introductionmentioning

confidence: 99%

Diversified Trajectory Pattern Ranking in Geo-Tagged Social Media

Yin¹,

Cao²,

Han³

et al. 2011

Proceedings of the 2011 SIAM International Conference on Data Mining

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show abstract

“…Giannotti et al [12] discover moving behaviors based on the transition time of trajectories, where their ROAs are identified by grid and point density. Choudhury et al [9] proposed a method to construct trajectories from photo data automatically. Wei et al [37] construct the top-k popular routes from uncertain trajectories using a mutual reinforcement approach.…”

Section: Related Workmentioning

confidence: 99%

Travel topic analysis: a mutually reinforcing method for geo-tagged photos

Kou¹,

U²,

Yang³

et al. 2015

Geoinformatica

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Sharing personal activities on social networks is very popular nowadays, where the activities include updating status, uploading dining photos, sharing video clips, etc. Finding travel interests hidden in these vast social activities is an interesting but challenging problem. In this work, we attempt to discover travel interests based on the spatial and temporal information of geo-tagged photos. Obviously the visit sequence of a traveler can be approximately captured by her shared photos based on the timestamps and geo-locations. To extract underlying travel topics from abundant visit sequences, we study a novel mixture model to estimate the visiting probability of regions of attractions (ROAs). Such travel topics can be used in different applications, such as advertisements, promotion strategies, and city planning. To enhance the estimation result, we propose a mutual reinforcement framework to improve the quality of ROAs. Finally, we thoroughly evaluate and demonstrate our findings by the photo sharing activities collected from Flickr TM .

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“…Finally other applications based on the geographical knowledge extracted from Web resources are trajectory mining and travel Itinerary mining for tourist purposes [40,126] and activity recommendation [74]. In addition, Silva and Martins [159] developed a tag suggestion method based on analysis of collective knowledge, spatial proximity extracted from picture collections and automatic assignment of geographical location to a tagged picture.…”

Section: Related Workmentioning

confidence: 99%

Geo-Temporal Mining and Searching of Events from Web-based Image Collections

Ruocco

2014

SIGIR Forum

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The proliferation of Web-and social media-based photo-sharing applications have not only opened many possibilities but also resulted in new needs and challenges. They have resulted in a large amount of personal photos being available for public access. One of the most interesting characteristics of these data is that they are surrounded by 1) textual annotations, also called tags, which are intended to describe and categorize, by collective user efforts, the uploaded resources 2) temporal information referring to when a pictures has been taken and often by 3) a locational information describing where the picture has been taken. Despite the recent developments and technological advances in Web-based media-sharing applications, the continuously increasing amount of available information has made the access to the photos a demanding task. In general, we can address this challenge by allowing photo collections to be organized and browsed through the concept of events. We also believe most users are familiar with searching photo collections using events as starting points.Aiming at supporting the detection and search of event-related photos, this thesis proposes a novel framework for extracting pictures related to real-life events from a collection of Web-images by leveraging on their temporal geographical and textual annotations and comparing the proposed approach with existing related state-ofthe-art approaches. Second, a set of geographical features is proposed describing the characteristics of the geographical profile of query terms deriving concepts from exploratory analysis. Third, the thesis provides two different tag-based search framework to improve the effectiveness of searching images related to events. The first framework is based on temporal and geographical proximity of query terms to the temporal neighbors of a given timestamped query, while the second framework is based on a novel machine-learning based query expansion method combining the heterogeneous textual, temporal and geographical similarity between query terms and candidate expansion terms for the selection of the expansion terms given a free text textual query.All the proposed methods have been evaluated by performing extensive experiments on real data gathered from media-sharing applications on the Web. Where possible, comparison with related techniques has been performed to reinforce the validity of the presented approaches. The proposed methods have shown promising results in both the extraction and clustering of event-related images and searching eventrelated pictures by using metrics from the state of the art.i ii Preface

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Constructing travel itineraries from tagged geo-temporal breadcrumbs

Cited by 45 publications

References 6 publications

Diversified Trajectory Pattern Ranking in Geo-Tagged Social Media

Diversified Trajectory Pattern Ranking in Geo-Tagged Social Media

Travel topic analysis: a mutually reinforcing method for geo-tagged photos

Geo-Temporal Mining and Searching of Events from Web-based Image Collections

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