travel route recommendation using geotagged photos

Knowl Inf Syst (2013) 37:37–60DOI 10.1007/s10115-012-0580-z

REGULAR PAPER

Travel route recommendation using geotagged photos

Takeshi Kurashima · Tomoharu Iwata ·Go Irie · Ko Fujimura

Received: 22 February 2011 / Revised: 21 July 2012 / Accepted: 9 October 2012 /Published online: 23 October 2012© Springer-Verlag London 2012

Abstract We propose a travel route recommendation method that makes use of thephotographers’ histories as held by social photo-sharing sites. Assuming that the collection ofeach photographer’s geotagged photos is a sequence of visited locations, photo-sharing sitesare important sources for gathering the location histories of tourists. By following their loca-tion sequences, we can find representative and diverse travel routes that link key landmarks.Recommendations are performed by our photographer behavior model, which estimates theprobability of a photographer visiting a landmark. We incorporate user preference and presentlocation information into the probabilistic behavior model by combining topic models andMarkov models. Based on the photographer behavior model, proposed route recommen-dation method outputs a set of personalized travel plans that match the user’s preference,present location, spare time and transportation means. We demonstrate the effectiveness ofthe proposed method using an actual large-scale geotag dataset held by Flickr in terms of theprediction accuracy of travel behavior.

Keywords Photographer behavior model · Geotag · Travel route recommendation

1 Introduction

Due to the proliferation of small digital cameras and mobile phone cameras, there has beengreat interest in online photo-sharing services such as Flickr [1] and Google Picasa [2].These services allow users to upload photographs and attach informative tags to them, andthey have succeeded in collecting large scale sets of tagged photographs from huge numbersof users. Geotags indicate where the photographs were taken, and are automatically capturedby the aforementioned photo devices or location-aware devices, or alternatively are specified

T. Kurashima (B) · G. Irie · K. FujimuraNTT Service Evolution Laboratories, NTT Corporation, Kanagawa, Japane-mail: [email protected]

T. IwataNTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan

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38 T. Kurashima et al.

by the user. Geotags are powerful metadata for introducing spatial information into Webapplications; recent research efforts have shown the potential of geotags by developing variousgeotag-associated applications such as georeferenced image search [23,24], automatic imagegeolocation [9,15,22], and georeferenced content browsing [5,11,21,33,34].

This paper focuses on developing a travel route recommendation method that utilizesgeotagged images in photo-sharing services. By sorting the photographs by their timestampsfor each user, the geotagged photographs yield personal travel route histories. The value ofthese travel route histories in geotagged photographs can be emphasized in terms of theirquality and quantity. The photographed geolocations are good recommendations in terms offinding attractive locations because the action of uploading a photograph can be regardedas a positive vote that the location is worth visiting. Moreover, according to our research,at least 40,000,000 geotagged photographs uploaded by over 400,000 users are available onFlickr. This tremendous volume of travel data is a rich source of travel routes that can matchvarious user preferences.

The major research problem addressed in this paper is generating recommended travelroutes from a given geotag collection. This consists of two sub-issues; one is learning themodel of travel routes from the geotag dataset, and the other is generating recommendedroutes from the model. The desired properties of recommended travel routes are not only thatthey be easily accessible, but also that they suit the users’ interest. We then propose a methodthat trains a probabilistic photographer behavior model; it takes account of both the user’scurrent location and user’s personal interest. We then formalize the sub-issue of recommendedroute generation. Given the past and/or future itineraries and the amount of spare time to spendon future travel, our method outputs a set of customized landmark sequences that match theuser’s preference, and the user’s present location and spare time. We present an effectivealgorithm for finding recommended travel routes based on the photographer behavior models.

We also implement an online application that helps the user to plan a new trip. Figure 1shows the current user interface of the application. Given a location history including thecurrent location, desired time to spend on future travel and means of transportation, thisapplication suggests a set of travel routes that might suit the user’s interest. Each tour planconsists of a travel route (a sequence of one or more landmarks) and the travel time betweenlandmarks. The travel time values change due to the variety in user-specified transportationmodes and are estimated from travel route histories. The total time satisfies the time conditiongiven by the user. The results are ranked by the likelihood scores assigned by the photographerbehavior model (in the lower part of Fig. 1).

Our major contributions in this paper thus include:

– A model for travel route recommendation that takes account of both the user’s currentlocation and user’s personal interest;

– A method and an application to provide a set of personalized travel routes that match theuser’s current location, user’s interests, user’s spare time and user’s means of transporta-tion;

– A method to estimate expected traveling times of some typical transportation modes suchas walking, taking public transportation and driving, between two locations.

– Detailed evaluations of the performance achieved against an actual large-scale geotagdataset held by Flickr.

This paper is organized as follows: The next section describes related works. Section 3defines the data model and our research problem and presents a method for travel routerecommendation. Section 4 shows the effectiveness of the proposed method using Flickrdata and Sect. 5 concludes this paper.

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Travel route recommendation 39

Fig. 1 The interface of the route recommender system. Upon being given a location history including presentlocation, free time duration, and modes of transportation, returns a ranked list of travel plans. The best routeis plotted on the map interface

2 Related work

The important research areas related to our work are geolocation recommender systems andgeotag-based applications. We study the latest reports that address these areas.

2.1 Recommender systems

There are a number of local Web search systems that can recommend location-specific contentto users. The main characteristic of these systems is that they can personalize their recom-mendations to the user. Given opening times and user ratings, Yahoo Travel [3] enables theuser to search for local information about sights to visit. Horozov et al. [19] proposed acollaborative-filtering-based method to recommend restaurants. The difference between ourwork and theirs is that ours generates recommendations without explicit user ratings. Theinformation used is a summary of knowledge acquired by photographers; such data includelandmarks, representative textual descriptions, photographs, routes, and travel times.

Data mining from GPS trajectories gathered by mobile devices is also related to therecommendation task because its main goal is to predict where a person may be going.Ashbrook et al. [6] apply a Markov model to GPS data in an attempt to model travelerbehavior; their traveler model always recommends the most chosen locations adjacent to thecurrent location. Krumm and Horvitz [25] also proposed a method for predicting the driver’sdestination based on multiple drivers’ GPS trajectories. Zheng et al. [36] applied a graphmining method to a GPS dataset generated by 107 users in order to extract a region-of-interestand classical travel sequences between them. Spatio-temporal sequential pattern mining from

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GPS trajectories was performed by Giannotti et al. [13]. The major differences between ourwork and theirs lie in the two aspects; one is that this work is the first to utilize geotaggedphotos in social photo-sharing sites for the purpose of route recommendation. Our method,which is based on large-scale personal databases automatically extracted from photo-sharingsites, dramatically increases the opportunity for the user to draw upon peoples’ experiences.The other is that this work shows how to learn the photographer’s personal interests fromgeotagged data; travel routes that are not only popular but also suitable for the user can berecommended. Efficient fastest path finding between given multiple destinations, describedin [26], addresses how to determine the fastest visiting order from user-specified multipledestinations, our study addresses how to determine the destinations.

2.2 Geotag-associated web applications

Various types of Web applications such as image search [23,24], content browsing[5,11,21,33,34], and geographic image annotation [9,15,22] leverage geotags in order tofacilitate and to enrich photo browsing. Different from these existing applications, this workpresents a new direction in the use of geotagged and time-stamped photographs, travel routerecommendation. It goes beyond browsing to support human activities in the real world.

2.2.1 Image search

In the current multimedia information retrieval research field, geolocation information istaking an important role in Web image search. Kennedy et al. [23,24] proposed methodsto extract representative and diverse location-specific photographs from Flickr based on tagco-occurrence, image similarity, and geolocation information.

2.2.2 Content browsing

Managing large-scale content collections through a spatial interface (e.g. Google Maps [4])are a promising approach to facilitate georeferenced content browsing. Recent research hasintroduced methods that arrange landmark tags and geotagged images according to spatialcoordinates [5,11,21,33,34]. One important work is World Explorer proposed in [5,21]; itfinds representative tags attached to location-specific photographs and visualizes them on amap interface. Similar work performed by Crandall et al. [11] allows users to browse all of theworld’s photographs on a single world map. Geographical thesauri, which are automaticallygenerated from user annotations, enable their system to visualize photographs in the mapinterface. Popescu et al. [30,31] also proposed a method for automatically creating a mul-tilingual geographical thesauri, called gazetteer, from a website for sharing georeferencedphotographs and Wikipedia metadata. They automatically identify geographical names, cat-egorize these names, find their GPS coordinates and measure their popularities. Snavelyet al. [33,34] proposed Photo tourism to enable full 3D navigation and exploration of a setof images. Their method reconstructs 3D structures of several landmarks from their relatedgeotagged photos.

2.2.3 Geographic image annotation

A central goal is geolocating one or more Web images that have no original geotags.The key is to estimate geometric location by analyzing image content. Hays and Efros[15] proposes a data-driven approach to exploit the Flickr geotagged photo collection.

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Their approach computes scene similarity between a target photo and each of the geotaggedphotos and attaches the geotags of similar photos to the target photo. Different from thedata-driven approach, the model-based approach assumes a probability (likelihood) modelof image features conditioned by geometric location. Cao et al. [9] proposed a method thatuses kernel canonical correlation and logistic regression to enhance semantic annotation andgeo-location information based on image content. The method proposed in [22] introducesthe photographers’ travel sequence prior. Combinatorial use of the prior and image likelihoodmodel offers better performance.

There are a few studies that use geotags and time-stamps for the automatic constructionof trips. Popescu et al. [32] discovered popular trips within a city from data in user-generatedphotographic collections. Similar work performed by [10] used the popularity of landmarks,stay times and transit times between landmarks in order to construct representative travelitineraries linking popular landmarks within a city. Popescu et al. [28] also proposed a methodfor deducing the typical visit duration of a landmark. The main difference between our workand theirs is that ours can recommend personalized travel routes/itineraries based on theinterests of the individual (what kind of landmarks did he/she visited in the past). Person-alization is not performed by [32] and [10]. Furthermore, our work presents a method forestimating traveling times between landmarks. Our method can change the estimated valueaccording to use’s transportation mode (e.g., walking, taking public transportation, driving).User’s information modeling has been reported before; Backstrom et al. [7] attempted topredict an individual’s living place (physical address), given the known geographical infor-mation of her/his friends uploaded on Facebook. Popescu and Grefenstette [29] analyzedtextual metadata associated with Flickr photos that unveil users’ home location and gender.Our model can generate recommendations based on the user’s implicit interests.

3 Travel route recommendation method

3.1 Formulation and problem definition

In this section, we describe the proposed recommendation method. Our approach is based onthe fact that the data uploaded to photo sharing sites are significant information sources forfinding landmarks and setting travel routes between them. Using these information sources,we can build photographer behavior models and apply them to make and recommend a planto newly arrived tourists. This recommendation is performed by understanding and modelingthe processes that underlie a photographer’s decision about what to do next throughout his/herjourney.

Suppose that we have a set of geotagged and time-stamped photographs {(lui , tu

i , vui )}τ u

i=1for each user in user set U . Our notation is summarized in Table 1. Our research problemcan be described as follows; given location history hu and the amount of spare time d tospend on future travel, the task is to recommend a set of personalized travel routes whichare sequences of geolocations {〈luk

τ u+1, . . . , lukτ u+T uk 〉}Kk=1 where the estimated time needed to

fully follow a sequence is d .We note that geolocation lu

i refers to the location of the i th photo of user u, but it can alsomark the geolocation of the photographed object. We also assume that the intervals betweenimages are correct; the original image timestamps are not assumed to be correct because thecamera’s clock may not be set correctly.

The general approach of our travel route recommendation framework is illustrated in Fig. 2.First, given a large collection of geotagged photos, we automatically find the landmarks that

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Table 1 Notation

Symbol Description

U Set of users

u User, u ∈ U

lui Geolocation of the i th photograph of user u represented by by latitude and longitude

coordinates in bi-axial spacetui Universal time of the i th photograph of user u

vui Set of annotation tags of the i th photograph of user u

τu Number of locations in the history of user u

hu Location history of user u, where geolocations are sorted by their timestamps, hu = 〈lu1 , . . . , lu

τu 〉K Number of routes to be recommended

T uk Number of locations in the kth recommended route

Photographer behavior modeling

Estimate the photographer's

interests

Find the popular routes between

landmarks

Combination

Location history

ul1ul2

uulτ

Landmark detection

Landmark B

Landmark C

Landmark Auh

Travel route generation

ul1ul2

uulτ

Location history uh

1

1

uul

+τ

Travel routes

1

2

uul

+τ

2

1

uul

+τ

),|( 1u

tt hllP −

Fig. 2 Travel route recommendation framework

were photographed often. These landmarks are interesting in terms of route planning sincethey are assumed to represent a positive vote by previous visitors. Found landmarks are thusappropriate candidates for recommendation.

Second, we model the behavior of the photographer to estimate the probability that thephotographer will visit a landmark. Our model is based on the two assumptions;

1. the geolocation that the tourist will move to next largely depends on the current andrecently visited locations.

2. the choice of what to do next is also determined by the traveler’s interests.

For example, a traveler currently in Times Square is more likely to visit the Statue of Libertythan the Golden Gate Bridge in San Francisco because the Statue of Liberty is much closer.A traveler who is interested in art is likely to visit the Metropolitan Museum of Art while atraveler who is interested in sports is likely to visit Yankee Stadium.

Finally, we use the probabilistic photographer behavior model built in the previous stepto recommend a set of landmark sequences. We recommend the route that is most likely tosuit the user. The following subsection details each step.

3.2 Step 1: photo clustering by mean-shift procedure

Given a collection of photos that contain their geolocations, represented by latitude andlongitude, we automatically extract often-photographed landmarks in the city because wewant to recommend popular landmarks. We define a landmark as a uniquely representedspecific location within the city; such as a sightseeing spot, a store, a building, a bridge, an

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outlet, and so on. Examples in the New York area include Union Square, Statue of Liberty,and Madison Square Garden.

The previous work by Crandall et al. [11] provides several techniques for the analysis ofgeotagged photo collections. They showed that the mean-shift procedure was very effectivefor landmark extraction from spatial data such as the information we consider here. Mean-shiftis a nonparametric feature-space analysis technique that uses kernel density estimation andhas been successfully applied to a wide range of vision and image processing applications. Theissue tackled here can be regarded as the problem of clustering points in a two-dimensionalfeature space, latitude and longitude. The benefit of this approach is that it requires only thescale of observation, unlike most clustering methods which require choosing some number ofclusters or making underlying distributional assumptions. Thus we use mean-shift clusteringto mine often-photographed landmarks.

The method automatically estimates the modes of underlying unobservable probabilitydistributions from a collection of observed geotagged photographs. The mode of each clusterindicates a landmark with high photo density. For given location l, the mean-shift vector isdefined as follows,

mw,g(l) =∑

u∑

i lui g

(‖ (l − lui

)/w‖2)

∑u∑

i g(‖ (l − lu

i

)/w‖2) − l, (1)

where g is the weight of each data point corresponding to some chosen kernel function,and w is a bandwidth parameter. We adopted a Gaussian distribution function as the kernelfunction. The mean-shift procedure computes a sequence starting from some initial locationl(1) where

l(c+1) = l(c) + mw,g(l(c)), (2)

which converges to a location that corresponds to a local maximum of the underlying distri-bution as the mean-shift vector approaches zero. A single landmark thus can be regarded asa virtual location characterized by a group of several geolocations.

Once we have extracted a set of landmarks, each location history, which is a sequence ofgeolocations, is transformed based on the results as follows:

1. Convert each geolocation in a location history into one of the landmarks found by themean-shift procedure. We use notation lu

i to indicate the landmark captured by the i thphoto of user u as well as the geolocation in the remainder of this paper.

2. Cluster successive geolocations in a location history; which represent that a photogra-pher took two or more photos successively at the same landmark without visiting otherlandmarks.

3. If 2 is performed, time value of a set of successive geolocations is updated as the medianof the photographer’s arrival and leaving times at the cluster.

The result of Step 1 is thus a set of landmarks and a set of location histories.

3.3 Step 2: photographer behavior model

We want to estimate the probability that user u at landmark lt−1 at time t − 1 visits lt at timet, P(lt |lt−1, hu), which represents the photographer behavior. In our model, we assume thatthe landmark to be visited next depends on the present location and the traveler’s interests.This dependence on location and interest can be treated using Markov and topic models,respectively. We model the photographer behavior by combining Markov and topic modelsin a probabilistic framework.

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3.3.1 Markov model

Markov models are widely used as probabilistic models that can handle sequential informa-tion. For simplicity, we use a first-order Markov model, but Markov models of any order canbe employed in a similar way. In the first-order Markov model, the next landmark dependson the previous landmark as follows,

P(lt |lt−1, lt−2, . . . , l1) = P(lt |lt−1). (3)

Probability P(lt |lt−1) can be calculated using maximum likelihood estimation as follows,

P(lt |lt−1) = N (lt−1, lt )

N (lt−1), (4)

where N (lt−1, lt ) is the number of times lt is visited after to lt−1 in the dataset, and N (lt−1)

is the number of times lt−1 is visited.

3.3.2 Topic model

A topic model is a hierarchical probabilistic model, in which a user is modeled as a mixtureof topics, and a topic is modeled as a probability distribution over landmarks. Topic modelshave been successfully used in a wide variety of applications such as information retrievaland language modeling [8,17] as well as modeling user interests [18,20]. We assume thatthere are Z topics and take Z to be a set of topics. In topic modeling, the probability that auser with location history hu visits location lt is calculated by the following equation underthe assumption of the conditional independence of hu and lt given latent topic z ∈ Z,

P(lt |hu) =∑

z∈Z

P(z|hu)P(lt |z), (5)

where P(z|hu) represents the interest of the user, which is the probability that user u isinterested in topic z, and P(lt |z) represents trends in a topic, which is the probability thatlandmark lt is chosen from topic z.

Several topic analysis techniques such as latent semantic analysis (LSA), probabilisticlatent semantic analysis (PLSA) [17], and latent dirichlet allocation (LDA) [8] have beendeveloped in the area of natural language processing. We adopt PLSA as the topic model inorder to estimate the topics of each sequence.

We use the EM algorithm to infer topic proportions P(z|hu) for z ∈ Z and u ∈ U , andlandmark probabilities P(l|z) for l ∈ L and z ∈ Z. Here, L represents a set of locations. Inthe E-step, we estimate the posterior probabilities for the latent topics as follows,

P(z|lt , hu) = P(z|hu)P(lt |z)∑

z′∈Z P(z′|hu)P(lt |z′) . (6)

In the M-step, we update the parameters so as to maximize the likelihood as follows,

P(z|hu) ∝∑

l∈L

N (l, hu)P(z|l, hu), (7)

P(l|z) ∝∑

u∈U

N (l, hu)P(z|l, hu), (8)

where N (l, hu) is the number of times landmark l occurs in history hu , and U is the setof users. By iterating E- and M-steps until convergence, we can obtain a topic model thatmaximizes the likelihood of the given data.

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3.3.3 Combining Markov and topic models

We combine Markov and topic models in order to incorporate both present location and userinterests into the photographer behavior model. Under the assumption that the history hu andlt−1 are independently conditioned on lt , the following approximation formula can combinethe two models,

P(lt |lt−1, hu) = P(lt |lt−1)

C(lt−1, hu)

P(lt |hu)

P(lt ), (9)

where P(lt |lt−1) and P(lt |hu) are obtained by Markov and topic models, respectively, P(lt )is the probability that landmark lt is visited, and C(lt−1, hu) is the normalization factor. Thistechnique is called unigram rescaling and has been used for language modeling [14]. P(lt )is calculated as follows,

P(lt ) = N (lt )

N, (10)

where N is the number of photographs.

3.4 Step 3: generating travel routes

In the previous step, we described a probabilistic photographer behavior model that predictsthe next location from the user’s present location and interests. We present below an effectivetravel route recommendation method that is based on the behavior model. A travel route isrepresented as a sequence of landmarks and the time intervals between them.

We generate and recommend X travel routes {〈luxτ u+1, . . . , lux

τ u+T ux 〉}Xx=1 that have highprobabilities for given user u,

P(〈luxτ u+1, . . . , lux

τ u+T ux 〉|luτ u , hu). (11)

The routes selected coincide with the present location and the interest of user u. If we calculatethe probabilities for all possible routes and sort them, we are sure of finding travel routesto recommend. However, this naive method requires excessive computational time, and itcannot be used in an interactive recommender system. Our solution is an effective route-finding method that enables users to search travel routes online. It is based on the best-firstsearch algorithm [12].

Algorithm 1 describes how to generate travel routes. The input to Algorithm 1 consists ofthe location history hu associated with user u, the amount of spare time to spend on futuretravel d , acceptable range ε, and the number of travel routes X . The function of Algorithm 1is to generate an array that stores X travel routes, where s is the sequence of visited locations,ds is the traveling time of s, ps is the chosen probability of s, slast is the last visited locationin s, and s+l is the updated sequence when location l is visited.

First we insert the present location luτ u into priority queue Q (line 4). A priority queue is a

data structure. When popping elements from the queue, the highest-priority one is retrievedfirst. Our queue is a special variant of the priority queue where an element’s priority isprobability value ps . We implement this by max heap. We get the highest-probability routes from Q and then decide whether it meets the time conditions specified by the user, lines6 to 10 in Algorithm 1. By using the max heap data structure, we can efficiently find thehighest-probability route. If the travel time of the current sequence does not satisfy thetime conditions, we search for a new route to other landmarks from the current location (lastvisited in the current sequence) and generate updated sequences, lines 11 to 17 in Algorithm 1.

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The chosen probability and traveling times of updated sequences are newly calculated by ourproposed photographer model (line 13) and traveling time estimation (line 14), respectively.T ravelT ime function returns the value of estimated traveling time between two locations(line 14). The values of traveling times are also learned from a set of location histories, anddetails are described in the next section (see Sect. 3.5). This process is repeated until thenumber of routes approaches X (line 18). Since probability P(l|slast, hu) is always less thanor equal to one, ps+l ≤ ps × P(l|slast, hu). Therefore, the set of X sequences found bythis best-first search algorithm holds the X highest-probability sequences from among allsequences that satisfy the condition.

Algorithm 1 Generate travel routesRequire: X > 0, d > 0 and ε > 01: Set an array A← �

2: Set x ← 03: Set a priority queue Q← �

4: Insert luτu into Q

5: repeat6: s ← get the highest-probability one from Q7: if d − ε ≤ ds ≤ d + ε then8: Push s into A9: x ← x + 110: end if11: if ds < d + ε then12: for l ∈ L do13: Set ps+l ← ps × P(l|slast, hu)

14: Set ds+l ← ds + T ravelT imeslast,l15: Insert s+l into Q16: end for17: end if18: until x = X19: Output A

3.5 Travel time estimation

When traveling, we usually have a variety of transportation options such as walking, takinga bus, taking a train, riding a bike and driving. Travel time from one location to anotherlargely depends on the user’s mode of transportation. Thus recommended travel plans arerequired to be changed in accordance with the user’s mode of transportation. For example,a place within walking distance should be recommended for a user who decides to walk tothe destination. To accomplish this requirement, we now try to estimate expected travelingtimes of some typical transportation means between any two spots.

Given a set of travel histories with timestamps, we aim to estimate expected traveling timesof typical transportation modes between any two spots. It would appear that photographers’real-life travel behaviors using different transportation means such as walking, taking a bus,taking a train, riding a bike and driving a car, are recorded on travel histories in photo-sharingsite. By assuming that there are K different transportation modes, we use K -means clusteringto distinguish one from the other and to derive the expected traveling time of each.

From location histories with timestamps, we first collect a set of time intervals betweeneach pair of locations: each data point represents the traveling time of one photographer.We then apply K -means clustering to the collected interval data and find K centroids.

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They are representative time intervals between the two spots and are assumed to be theextracted traveling times of K different transportation methods. By applying the estimatedtraveling time to Algorithm 1 (line 14), our system can generate proper travel routes inresponse to the user indicated mode of transportation.

4 Experiments

This section evaluates the performance of the proposed method by conducting four experi-ments on a Flickr-sourced geotag dataset. In the first experiment, we analyze the performanceof our method with different parameter settings. The second experiment evaluates the one-step prediction accuracy of our probabilistic photographer behavior model by comparing itto those of three other probabilistic models. In the third experiment, we analyze the multiple-step prediction accuracy. The fourth experiment addresses the characteristics of our traveltime estimation method. Moreover, we show some examples of recommended travel routeson our online application.

4.1 Flickr dataset

The datasets used in these experiments were collected by downloading photo metadata fromFlickr [1] using the site’s public API. The crawled data consist of 696,394 photographs andtheir associated metadata, which were taken by 71,718 unique users. All were taken in theEast Coast and the West Coast of the United States between January 1st, 2006 and June 31st,2009.

Details of the data collection procedure are as follows. We first chose several major citieson each coast; Washington D.C., New York City, Philadelphia and Boston on the West Coast;Los Angeles, San Francisco and Las Vegas on the East Coast. For each city, geotagged photostaken within 20 km from its center were crawled. Given geo queries, which consisted of alatitude and a longitude, the Flickr API returns a collection of geotagged photos taken in thespecified region. This is an efficient way to crawl the geotagged photos within each regionbecause these major cities have been reported to be the top metropolises in the United Statesby a previous work [11]. We then extracted landmarks in each city by applying the mean-shift algorithm (see Sect. 3.2). In this algorithm, as bandwidth parameter w is decreased,more landmarks are detected. In order to evaluate the performance of our method at severaldifferent scales, we set two different bandwidth parameters: w = 0.0005 (50 m) and 0.0001(10 m). Finally, for each region and scale, the geotags (longitude and latitude) sequences ofall users were translated into landmark sequences, that is travel routes. Detailed informationfor the number of users (sequences) and the number of landmarks are shown in Table 2. Notethat we omitted the users (sequences) who visited fewer than five landmarks and landmarksthat were captured by fewer than three users.

4.2 Experiment 1: parameter influence

One parameter that influences the performance of our method is the number of topics intro-duced in the topic model. Therefore, we determined the best value of this parameter by using5-fold cross-validation. Specifically, one-fifth of all sequences (users) were used for testing,and the others were used for training. In the testing, we predicted the last visited landmark ofeach user in the test examples based on her/his landmark sequence excluding the last visitedlandmark since our model predicts the next landmark likely to be visited. According to a

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Table 2 Information about generated travel histories and the landmarks found by mean-shift procedure

Region-scale The number of sequences The number of landmarks Average length of sequences

East—50 m 9,267 414 14.02

West—50 m 6,450 316 14.05

East—10 m 11,354 1,419 15.82

West—10 m 7,913 1,119 15.65

The users (sequences) who visited fewer than five landmarks and landmarks that were captured by fewer thanthree users were omitted

(a) (b)

(c) (d)

Fig. 3 Precision at different numbers of topics. The Y axis plots the averaged precision across 5 runs.The datasets are a the East coast at bandwidth = 50 m, b the West coast at bandwidth = 50 m, c the Eastcoast at bandwidth = 10 m, and d the West coast at bandwidth = 10 m

previous survey, the precision measure is by far the most commonly used measure for evalu-ating the performance of recommender systems [16]. Accordingly, we used the precision ofpredicted landmark as the performance measure. That is to say, we calculated the percentageof correct predictions over all test examples. This step was repeated 5 times using each subsetas a test data.

We used the data shown in Table 2. For each dataset (each region and scale), we determinedthe best parameter value (the number of topics). The results of 5-fold cross-validation areshown in Fig. 3. In the figure, the X axis plots the number of topics and the Y axis plotsthe precision score averaged across 5 runs. The number of topics of the proposed modelwere set to 3–9 and 10–50 in steps of 5. The topic model (PLSA) was also tested using the

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Table 3 Best parameter value (the number of topics) derived in the first experiment

Region-scale Topic model Markov-topic model

East—50 m 9 9

West—50 m 8 10

East—10 m 9 15

West—10 m 15 15

same condition. As shown in this figure, the proposed model and topic model have curves ofsimilar shape. When the number of topics is small, the average precision is low. The averageprecisions of both methods are maximized at about 10, and after that, it gradually decreaseswith further increases in the number of topics. It suggests that setting the appropriate numberof topics, that is user’s interests, contributes to yielding better recommendation performance.The optimal parameters derived in this experiment, summarized in Table 3, are used insubsequent performance comparisons.

4.3 Experiment 2: One-step prediction accuracy

In this experiment, we compared the following four probabilistic models including our pho-tographer behavior model:

Multinomial model predicts the next landmark based on its popularity. Popularity canbe calculated using the multinomial probability distribution over landmarks P(l). Themost visited landmark within the region, except for the current location, is always rec-ommended. This model does not consider either the user’s current location or interests.Markov model predicts the next landmark based on the user’s current location, which iscalculated by the Markov model P(lt |lt−1). The most chosen landmark, given the currentlandmark, is always recommended. This model considers the user’s current location butdoes not consider the user’s interest.Topic model predicts the next landmark based on user interest, which is calculated by thePLSA model P(l|hu). This model considers the user’s interest, but not the user’s currentlocation.Markov-Topic model (proposed photographer behavior model) predicts the next land-mark based on both the user’s current location and interest, which is calculated by com-bining the Markov model and the topic model using the unigram rescaling technique.

We used the data shown in Table 2 for training and testing of the model; training dataconsisted of landmark sequences excluding the last visited landmarks of all users, and the testdata were the last visited landmarks of all users. Thus, the number of training data equals thenumber of sequences (users) in Table 2, and the number of test data also equals the number ofsequences (users) in Table 2. The performance measure is the same as in the first experiment(i.e., we calculated the percentage of correct predictions over all users). The results are shownin Fig. 4. In this figure, the X axis plots the dataset and the Y axis plots the precision score. Weused the sign test to confirm the statistical significance between the proposed model and theother baselines. 1To sum up, for both datasets, the proposed model (Markov-Topic model)yields better precision than Multinomial, Topic, and Markov model, and the differences aresignificant (two-sided sign test: p < 0.01). The result shows that the proposed method can

1 In this paper, all of the statistical tests were conducted using the R and R libraries at http://www.r-project.org/.

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(a) (b)

Fig. 4 Comparison of precisions. The datasets are a the East coast and the West coast at bandwidth = 50 mand b the East coast and the West coast at bandwidth = 10 m

appropriately predict the traveler’s location since it uses both the user’s current location andhis/her interest. The precision scores are not very high, but our system can actually meet moreof the user’s information needs since it suggests more than one recommendation to the user.In this experiment, we checked whether just one recommendation of the system matched tothe last visited landmark.

4.4 Experiment 3: multiple-step prediction accuracy

To cover practical scenarios, our method generates and outputs travel routes dependingon given spare time; each is a landmark sequence of certain length (see Sect. 3.4). Thisexperiment evaluates the appropriateness of the recommended travel routes under differentspare time conditions. This experiment is based on the four datasets used in the experimentsabove. For each dataset, the test data was created by collecting the last part of each sequencewithin the given spare time s. The training dataset consisted of the set of sequences excludingtest data part. To be exact, the number of training data (the number of test data) used inthis experiment was slightly different from that of the previous experiments since only thesequences whose traveling times exceeded the given spare time were used as training data(test data). Detailed information about sequences and landmarks used in this experiment isshown in Table 4.

We measured the difference between the generated routes and each test sequence. Theedit distance 2 is thus applied as the evaluation metric since it measures the distance betweentwo sequences in terms of the minimum number of edit operations required to transformone sequence into the other [27]. The allowable edit operations are inserted into a sequence,delete from a sequence, and replace one landmark with another. We compared our Markov-topic model with the other models, multinomial model, topic model, and Markov model. Inthis experiment, we use average traveling time between two locations as estimated from the

2 Edit distance is also known as Levenshtein distance.

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Table 4 Information about travel histories and the landmarks used in experiment 3

Region-scale Time period (h) The number of sequences The number of landmarks

East—50 m 2 9,142 414

3 9,124 414

4 9,115 414

5 9,102 414

West—50 m 2 6,349 316

3 6,346 316

4 6,333 316

5 6,323 316

East—10 m 2 11,139 1,419

3 11,102 1,418

4 11,074 1,418

5 11,049 1,418

West—10 m 2 7,763 1,117

3 7,741 1,117

4 7,723 1,117

5 7,704 1,117

The users (sequences) who visited fewer than five landmarks and landmarks that were captured by fewer thanthree users were omitted

location histories. Figure 5 shows the performance of each model at four different spare times(2, 3, 4 and 5 h). The results show that the proposed method (Markov-Topic model) offersthe highest accuracy (i.e., the lowest edit distance). We also tested the statistical significanceof the difference between the average edit distances of the proposed and the baselines usingthe Wilcoxon signed-rank test. For each region, bandwidth, and spare time condition, theresult of the Wilcoxon signed-rank test is p < 0.01 (two-sided test). The results show thatthe proposed method is significantly better than the baselines in terms of route predictionaccuracy.

4.5 Experiment 4: estimating travel time of transportation mode

Our method can estimate the traveling times of several modes of transportation between twolandmarks (see Sect. 3.5). In this experiment, we discuss the characteristics of our expectedtraveling times by comparing them to the output of an existing conventional local searchservice. This experiment is based on the two datasets; travel histories in the East coast andthe West coast at bandwidth = 50 m.

Fifteen popular travel routes between two locations are first extracted from the dataset.The popularity of travel route is measured by counting the unique number of photographerstaking the route. For each major route, we calculate and collect a set of time intervals basedon location histories with timestamps. Our datasets include histories of photographers whodid not take a direct route, so that we eliminated the time intervals more than one and a halfhours. For each route, K major traveling times are then extracted. The number of clustersK is an input parameter of K -means clustering. In our experiment, K is set as 3 becausewalking, taking public transportation and driving are major transportation modes. In fact,three peaks in the frequency distribution of travel times are observed in some of major travel

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(a) (b)

(c) (d)

Fig. 5 Average edit distances of recommended travel route at user-specified time periods. The datasets are athe East coast at bandwidth = 50 m, b the West coast at bandwidth = 50 m, c the East coast at bandwidth =10 m and d the West coast at bandwidth = 10 m

routes (see Fig. 6). Furthermore, existing local search service such as Google maps returnsthese three results. Public transportation can include taking a train, taking a bus and walking.In most cases, the time of arrival decreases in the following order; walking, taking publictransportation and driving. Thus we use the longest (3rd) travel time extracted by K -means asthe travel time of walking, second shortest travel time as that of taking public transportation,and shortest travel time as that of driving. We compared the expected traveling times extractedfrom our datasets to those yielded by Google maps. Given a start point and end point, it cantell us the expected traveling times of these three transportation modes [4].

The results are shown in Fig. 7. In this figure, the Y axis plots the average traveling timeover the top 15 popular travel routes. For each transportation mode, average traveling time iscompared to that of Google maps. For both datasets, estimated time values using our methodare a touch longer than that yielded from Google maps. Average difference values betweenthe two methods are also shown in Fig. 8. Traveling time estimated from location historiesmay be reasonable for travel planning because (1) it includes the average time spent at eachlocation, not just transfer time, and (2) it is considered to be the estimated values consideringtraffic and weather conditions.

4.6 Example of route recommendation

We implemented our proposed method and constructed a route recommender system thathelps the user in planning trips. We would like to show some examples of recommended travelroutes output by the system and discuss the effect of user information on personalization.

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Fig. 6 Frequency distributions of travel times between two landmarks. Top 3 popular travel routes in ourdatasets are captured

(a) (b)

Fig. 7 Comparisons of average travel times over the top 15 popular travel routes at several transportationmodes. The datasets are a the East coast at bandwidth = 50 m and b the West coast at bandwidth = 50 m

(a) (b)

Fig. 8 Average difference values between two methods (Proposed and Google maps) at several transportationmodes. We subtract travel time of Google maps (just transfer time) from estimated travel time by our method.The datasets are a the East coast at bandwidth = 50 m and b the West coast at bandwidth = 50 m

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Fig. 9 Examples of travel routes at different user-specified time periods. The top ranked plan is plotted on themap interface. The departure point of the user is the Library of Congress in Washington D.C. User-specifiedtime periods are 3 and 5 h. Bandwidth parameter is 10 m

As shown in Fig. 1, our recommender system was implemented as a map-based system.Given a geographical region on the map interface, the system extracts landmarks wheremany photographers took photos within the area specified, and visualizes them on the mapinterface. Each icon on the map represents a landmark, and a travel history and a currentlocation is identified by choosing landmarks in turn.

Note that representative textual descriptions for each landmark such as pier35, golden-gatebridge and brooklynbridge are automatically extracted from the set of annotation tags.Crandall et al. [11] showed a simple method for extracting representative tags of each land-mark cluster; we follow their strategy. For each landmark l, the score of tag v is calculatedby;

T agScorel(v) = P(l|v) = N (v, l)

N (v), (12)

where N (v, l) is the number of photos at landmark l that have tag v, and N (v) is the numberof photos that have tag v in the dataset. Note that we discard any tags that do not occur in atleast 5 % of the photos in the landmark cluster because they are a significant source of noise.

4.6.1 Recommendation based on user’s situation

Figure 9 shows recommendation examples when the user is at the Library of Congress inWashington D.C. As shown in Fig. 9, our recommendation algorithm can provide differentplans that well match the user’s spare time. When the user has only a little time (user-specified time is 3 h), the system recommends landmarks in the center of town such asLincoln Memorial, the Thomas Jefferson Memorial and so on. When circumstances allow(user-specified time is 5 h), the system arranges a tour of places in the region (i.e., longertravel routes are likely to be generated). The current approach to acquiring local informationis to read tour guides, magazine articles, local portal sites, and other commercial sources.These media certainly introduce famous travel routes, but they may require much more timethan the user can spare. Our recommender system can flexibly recommend travel routesthat suit the user’s available time. In addition, recommended travel plans are also changed

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Fig. 10 Examples of travel routes at different transportation modes. The top ranked plan is plotted on themap interface. The departure point of the user is the Pier 39 in Sanfrancisco. User-specified time period is 2 h.Bandwidth parameter is 10 m

in accordance with the user indicated mode of transportation. Figure 10 shows examplesof travel routes at different transportation modes, and the departure point of the user is thePier 39 in Sanfrancisco. Our personalization method based on user’s current location, user’savailable time and user’s transportation mode leads travelers to making a smart travelingdecision.

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Fig. 11 Examples of personalized routes in New York city. a shows recommended routes output by Markovmodel. b shows recommended routes output by Markov-Topic model (proposed model). User’s current locationis Times Square and spare time is 3 h. Chelsea, SoHo, DUMBO, Brooklyn Museum and the Lower East Sideare set as the user’s travel history. Bandwidth parameter is 10 m

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4.6.2 Recommendation based on user’s interest and situation

We show an example of a travel route generated for a user who is interested in art and discussthe impact of user interest on the likelihood attribute of the photographer behavior model.In this experiment, five landmarks connected with art, Chelsea, SoHo, DUMBO, BrooklynMuseum and the Lower East Side in the New York City, are set as the user’s travel history inorder to specify the user’s interest. User’s spare time is 3 h. User’s current location is TimesSquare because it is the largest landmark in our dataset. Figure 11 contrasts the recommendedroutes output by the Markov model to those of the proposed method (Markov-Topic model).As shown in Fig. 11, the Markov model recommends typical routes including famous placessuch as ground zero, the West Village and Brooklyn Bridge because it considers only thecurrent location and spare time (i.e., it does not consider user’s interest). In our experiment,these recommended landmarks are actually ranked among the top five major landmarksin New York City sorted by the number of visitors. The proposed method (Markov-Topicmodel), on the other hand, recommends routes that include landmarks concerned with artsuch as the American Museum of Natural History, the Metropolitan Museum of Art, andBroadway. Furthermore, they are all relatively accessible to the user because our proposedis combined with the Markov model.

5 Conclusions

This paper introduced a framework for travel route recommendation based on the large-scalesets of geotagged and time-stamped photographs held by photo-sharing sites. We assumethat the geotagged photographs represent personal travel route histories and sort the locationsindicated by the photographs according to their timestamps. To learn from these personalhistories, we presented a new technique; constructing probabilistic photographer behaviormodels that can estimate the probability of a photographer visiting a landmark. Based oninsights into photographer behavior, two models are combined in the photographer behaviormodel: one is the topic model, which estimates the user’s own personal preference; the other isa Markov model, which can find typical routes of photographers. We conducted quantitativeexperiments on a large-scale dataset to compare our probabilistic photographer behaviormodel against three probabilistic models in terms of one-step and multiple-step predictionaccuracy. The results demonstrate the effectiveness of the proposal in terms of its predictionaccuracy. We also estimate the expected traveling times via typical transportation modesbetween any two spots from the large-scale sets of travel histories. The characteristics ofexpected travel time are that it takes into consideration the average time spent at each location,traffic conditions, and weather conditions, so it may be reasonable for travel planning. Finally,we demonstrated that our recommendation method outputs a set of landmark sequences, ortravel routes, that coincide with the user’s preference (interest), present location, spare time,and transportation means. Different from the previous works, which used these metadata inorder to enhance the user’s experience when photo browsing, this work represents a newdirection in the use of the many geotagged and time-stamped photos available on the Web;we use them to enhance the experience of each traveler in the real world.

Our major future work is to utilize the attributes of the social network of photographersand each photographer’s profile (e.g., gender, home location). People with similar interesttend to connect to each other and people of similar interest are more likely to be friends,so a traveler’s decision about where and what to visit next is influenced a lot by his/hersocial neighbors. Each user’s profile is also important for estimating social neighbors in

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the real world. There is the possibility that these sources will improve the accuracy withwhich photographer behavior can be predicted. Actually, Yang et al. [35] have shown thatconsidering the friendship network leads to better recommendation performance. Anotherdirection is to incorporate content-based features of places into our model. For example, wewill be able to learn user’s activity area where her home, office, and other personal placesare located from geographical coordinates (GPS points) of visited landmarks. Based on theprofile, we will be able to change recommendations according to the user’s situation (i.e.,whether the user is currently performing daily life activities or is traveling). Furthermore, byconsidering location accessibility relative to the user (i.e., how far is it from the user’s activityarea), the degree of user’s interest in landmarks will be inferred effectively. Other landmarkfeatures such as the opening hour, the congestion factor and the fee are also important factorsin trip planning, and our future system will consider them.

Our future work also includes an evaluation in the field. The offline evaluations usingbehavior log data conducted in our experiments are an important starting point for exploringthe validity and effectiveness of our model. Actually, according to a previous survey, manyrecommender systems are evaluated offline [16]. However, offline evaluations are only oneapproach to evaluating models and recommendation methods. We will let real users in realisticmobile settings evaluate the real strength of our approach.

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Author Biographies

Takeshi Kurashima is a researcher at NTT Corporation, Japan. Hereceived the B.S. degree from Doshisha University, Japan, in 2004, theM.S. degree from Kyoto University, Japan, in 2006. He is currentlypursuing the Ph.D. degree at the Department of Social Informatics,Graduate School of Informatics, Kyoto University, Japan. His researchinterests include data mining and recommender systems.

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Tomoharu Iwata received the B.S. degree in environmental informa-tion from Keio University in 2001, the M.S. degree in arts and sci-ences from the University of Tokyo in 2003, and the Ph.D. degree ininformatics from Kyoto University in 2008. He is currently a researchscientist at Learning and Intelligent Systems Research Group of NTTCommunication Science Laboratories, Kyoto, Japan. His researchinterests include data mining, machine learning, information visualiza-tion, and recommender systems.

Go Irie received the B.E. and the M.E. degrees from Keio University,Japan, in 2004 and 2006 respectively, and his Ph.D. from the Universityof Tokyo, Japan, in 2011. He is currently a researcher at NTT Corpora-tion, Japan. Since 2012 he has been a visiting researcher at ColumbiaUniversity. His current research interests include multimedia analysisand applications.

Ko Fujimura is a professor at Otsuma Women’ University. Hereceived B.E. and M.E. degrees in electrical engineering in 1984 and1986, respectively, and D.Eng. degree in information engineering in1989, all from Hokkaido University. Previously he was the leader ofthe Data Mining Research Group in NTT Cyber Solutions Laborato-ries. From 2001 to 2011, he was a visiting professor at the Universityof Electro-Communications’ graduate school of information systems.His research interests include graph mining, information visualization,and interactive media. He is a member of the IEICE and IPSJ.

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travel route recommendation using geotagged photos

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