Mining people's trips from large scale geo-tagged photos

Arase, Yasuji; Xie, Xing; Hara, Takahiro; Nishio, Shojiro

doi:10.1145/1873951.1873971

Cited by 109 publications

(48 citation statements)

References 14 publications

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“…For instance, Pladino et al quantified both the global and local attractiveness of several famous tourism destinations using information from geo-tagged photos [13]. Arase et al identified people's frequent trip patterns, i.e., typical sequences of visited cities and stay durations, as well as descriptive tags that characterize the trip patterns [14]. They defined six trip themes (i.e., Landmark, Nature, Event, Gourmet, Business, Local) and mined frequent trip patterns on each theme at the city level.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu

et al. 2017

IJGI

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Abstract:With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platforms like Flickr have provided a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists and then extends the concept of motifs from topological spaces, to temporal spaces and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.

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Section: Introductionmentioning

confidence: 99%

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu

et al. 2017

IJGI

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“…These data usually consist of latitude and longitude coordinates, though they can also include altitude, bearing, distance, accuracy data, and place names. They are extremely valuable for application to structure the data according to location and for users to find a wide variety of location-specific information [1,17]. Considering that a place is generally a venue, we assume that at any given place and time there is a single event taking place.…”

Section: Query By Geotagmentioning

confidence: 99%

“…In [9], Liu proposed a social image retagging approach that aims to assign better content descriptor to the social images and remove noise description. In [1], Arase et al propose a method to detect people's trip based on their research of geo-tagged photos.…”

Section: Related Workmentioning

confidence: 99%

Finding media illustrating events

Troncy

Huet

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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We present a method combining semantic inferencing and visual analysis for finding automatically media (photos and videos) illustrating events. We report on experiments validating our heuristic for mining media sharing platforms and large event directories in order to mutually enrich the descriptions of the content they host. Our overall goal is to design a web-based environment that allows users to explore and select events, to inspect associated media, and to discover meaningful, surprising or entertaining connections between events, media and people participating in events. We present a large dataset composed of semantic descriptions of events, photos and videos interlinked with the larger Linked Open Data cloud and we show the benefits of using semantic web technologies for integrating multimedia metadata.

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“…Location-based Services (LBS), such as Foursquare 1 and Gowalla 2 , allow users to perform check-in actions that pin the geographical information of current location and time stamp onto their personal pages. The rapid accumulation of user check-in records can not only collectively represent the real-world human activities, but also serve as a great resource for location-based recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

“…Since the user-moving records implicitly reveal how people travel around an area with rich 1 Fouresquare: https://foursquare.com/ 2 Gowalla: http://gowalla.com/ spatial and temporal information, including longitude, latitude, and check-in timestamp, one reasonable application leveraging such user-generated check-in data is to recommend the travel routes. Indeed, much existing work recommends routes using GPS trajectories [2] [14] or geo-tagged photos [1][4] [18].…”

Section: Introductionmentioning

confidence: 99%

Exploiting large-scale check-in data to recommend time-sensitive routes

Hsieh

Lin

2012

Proceedings of the ACM SIGKDD International Workshop on Urban Computing

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Location-based services allow users to perform geo-spatial checkin actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale check-in data. Given a query location with the starting time, our goal is to recommend a time-sensitive route. We argue a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a goodness function which aims to measure the quality of a route. Equipped with the goodness measure, we propose a greedy method to construct the time-sensitive route for the query. Experiments on Gowalla datasets demonstrate the effectiveness of our model on detecting real routes and cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.

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Mining people's trips from large scale geo-tagged photos

Cited by 109 publications

References 14 publications

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Finding media illustrating events

Exploiting large-scale check-in data to recommend time-sensitive routes

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