Travel route recommendation using geotags in photo sharing sites

Kurashima, Toshio; Iwata, Tomoharu; Irie, Go; Fujimura, Kikuo

doi:10.1145/1871437.1871513

Cited by 278 publications

(204 citation statements)

References 21 publications

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“…Current major applications of route recommendation targeting pedestrians are evacuation guidance [10], [26] and traveling support [16], [19], [31]. Regarding evacuation, Inoue et al have proposed a system for indoor evacuation using mobile devices [10].…”

Section: Route Recommendation For Pedestriansmentioning

confidence: 99%

“…Regarding evacuation, Inoue et al have proposed a system for indoor evacuation using mobile devices [10]. For traveling support, location information such as GPS (Global Positioning System) log [31] and geotag information [16], [19] is the most important resource to find famous spots and typical routes for sightseeing. Zheng et al have applied clustering to GPS logs of many people in order to extract sightseeing spots in which many people are interested as well as common travel sequences [31].…”

Section: Route Recommendation For Pedestriansmentioning

confidence: 99%

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Walking Route Recommender for Supporting a Walk as Health Promotion

Takama

Sasaki

Okumura

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a walking route recommender system aiming at continuously supporting a user to take a walk as means for health promotion. In recent years, taking a walk becomes popular with not only the elderly, but also those from all ages as one of the easiest ways for health promotion. From the viewpoint of health promotion, it is desirable to take a walk as daily exercise. However, walking is very simple activity, which makes it difficult for people to maintain their motivation. Although using an activity monitor is expected to improve the motivation for taking a walk as daily exercise, it forces users to manage their activities by themselves. The proposed system solves such a problem by recommending a walking route that can consume target calories. When a system is to be used for long period of time for supporting user's daily exercise, it should consider the case when a user will not follow the recommended route. It would cause a gap between consumed and target calories. We think this problem becomes serious when a user gradually gets bored with taking a walk during a long period of time. In order to solve the problem, the proposed method implicitly manages calories on monthly basis and recommends walking routes that could keep a user from getting bored. The effectiveness of the recommendation algorithm is evaluated with agent simulation. As another important factor for walking support, this paper also proposes a navigation interface that presents direction to the next visiting point without using a map. As users do not have to continuously focus on the interface, it is not only useful for their safety, but also gives them room to enjoy the landscape. The interface is evaluated by an experiment with test participants.

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Section: Route Recommendation For Pedestriansmentioning

confidence: 99%

Section: Route Recommendation For Pedestriansmentioning

confidence: 99%

Walking Route Recommender for Supporting a Walk as Health Promotion

Takama

Sasaki

Okumura

et al. 2017

IEICE Trans. Inf. & Syst.

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“…Kurashima et al use the list of locations visited in the past by a user to incrementally build new trajectories that maximize their likelihood in the mixed topic-Markov model [6]. The best k routes satisfying a maximum time and distance constraints are returned.…”

Section: Related Workmentioning

confidence: 99%

A Trajectory-Based Recommender System for Tourism

Baraglia

Frattari

Muntean

et al. 2012

Active Media Technology

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Abstract. Recommendation systems provide focused information to users on a set of objects belonging to a specific domain. The proposed recommender system provides personalized suggestions about touristic points of interest. The system generates recommendations, consisting of touristic places, according to the current position of a tourist and previously collected data describing tourist movements in a touristic location/city. The touristic sites correspond to a set of points of interest identified a priori. We propose several metrics to evaluate both the spatial coverage of the dataset and the quality of recommendations produced. We assess our system on two datasets: a real and a synthetic one. Results show that our solution is a viable one.

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“…Furthermore, tools for analysis and prediction of movement can be useful for numerous applications. For example, travel models can help in predicting the spread of disease [6,15]; surveying tourism [9,10], traffic [2,14], and special events mobility for urban planning [3]; geolocating with computer vision [4,16]; interpreting activity from movements [19,22]; and recommending travel [7,17]. Analysis of travel data can also play a role within the larger context of analysis of social network data.…”

Section: Introductionmentioning

confidence: 99%

Learning Latent Factor Models of Travel Data for Travel Prediction and Analysis

Guerzhoy

Hertzmann

2014

Advances in Artificial Intelligence

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Abstract. We describe latent factor probability models of human travel, which we learn from data. The latent factors represent interpretable properties: travel distance cost, desirability of destinations, and affinity between locations. Individuals are clustered into distinct styles of travel. The latent factors combine in a multiplicative manner, and are learned using Maximum Likelihood. We show that our models explain the data significantly better than histogrambased methods. We also visualize the model parameters to show information about travelers and travel patterns. We show that different individuals exhibit different propensity to travel large distances. We extract the desirability of destinations on the map, which is distinct from their popularity. We show that pairs of locations have different affinities with each other, and that these affinities are partly explained by travelers' preference for staying within national borders and within the borders of linguistic areas. The method is demonstrated on two sources of travel data: geotags from Flickr images, and GPS tracks from Shanghai taxis.

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Travel route recommendation using geotags in photo sharing sites

Cited by 278 publications

References 21 publications

Walking Route Recommender for Supporting a Walk as Health Promotion

Walking Route Recommender for Supporting a Walk as Health Promotion

A Trajectory-Based Recommender System for Tourism

Learning Latent Factor Models of Travel Data for Travel Prediction and Analysis

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