Markwell (1997) argues that even the stereotype of tourists carrying big cameras, lenses and tripod is in some way a sign of the ineluctable relationship between tourism and photography. Actually, while on the one hand photographic representations of destinations and tourism attractions are there to inspire a tourist’s visit to a destination on the other hand taking pictures represent the major focus of activity for the tourist (Jenkins, 2003).
Moreover as described in Garrod (2009) there is a strong correspondence between images promoted by tourism industry and those taken by tourists. The research of Garrod (2009) confirmed the study of one other author: Urry (1990) highlighted the iconic status of the scenery and of the things represented in tourism photography (e.g. the UK red telephone box) highlighting the facts that one the one hand tourism seems to be essentially about virtually “consuming palaces” (i.e. before visiting them) and on the other hand the importance of photography for travelers (i.e. after visiting a place) is essentially related to the demonstration of the actual trip: traveler shows their own version of the (iconic) place they have seen before the trip.
At the destination level, image has an influential aspect in supporting travelers’ decision-making process (Choi, 2007) and even if brochures may have been the predominant vehicle of tourist imaginaries (Hunter, 2008), nowadays websites and new technologies are also incorporating hedonic messages, as underlined by Gretzel and Fesenmaier in 2003 which stated that new technologies to be effective need to incorporate sensory information
As travel and tourism are experience-based activities (e.g. Tussyadiah & Fesenmaier, 2008) such experiences need to be communicated. Communities, blogs, travel review websites and social media in general offer publication outlets to help information sharing among users (Arsal et al., 2008). These websites increasingly gain substantial popularity in online travellers’ use of the internet (Gretzel, 2006; Pan et al., 2007).web2.0 does not provide any new protocol or completely new technologies (although a range of related technologies has been developed around it, like Ajax). It represents mainly a different use of the web itself, characterized by different expectations, goals and practices (Kolbitsch and Maurer, 2006): (i) the web is conceived more as a public square where to connect and exchange opinions instead of a library, (ii) the possibility of publishing contents has been widespread thanks to easy-to-use websites and applications and (iii) the availability of large bandwidth connections makes possible a wider use of multimedia, leading to good quality, interactive content provided by the users themselves (Cantoni and Tardini, 2009).
Social media, are playing an increasingly important role as information sources for travelers as they increasingly appear in search engine results in the context of travel-related searches (Hays, Page, Buhalis, 2012). Social media constitute a substantial part of the search results and therefore traditional providers of travel-related information will have to ensure that they include social media in their online marketing (Xiang and Gretzel, 2010). Looking forward, successful tourism organisations will increasingly need to rapidly identify consumer needs and to interact with prospective clients by using online, comprehensive, personalised and up-to-date communication media for the design of products that satisfy tourism demand.
Flickr.com is considered the most relevant and popular image sharing social network (Alexa rank 34, US rank 26, enjoying 730’248 link in to date). Moreover, within social media, user generated travel pictures are carrying a lot of information because on the one hand they are often described by sets of small terms called “tags” (which once collected among the users within a system constitute a folksonomy) and on the other hand they often represent places within a map (so they are geo-located).
Among the three approaches, collaborative and content-based filtering usually require a huge number of training examples to work properly. This is the reason of the so-called bootstrapping problem, that is the problem of providing good recommendations without already having a strong user base. The approach described in this paper, which relies on external knowledge (the one extracted from tags applied to geolocated photos), is a knowledge-based one, and as such it might represent a possible solution to the problem of recommending destinations without incurring in the bootstrap problem.
In other words the study investigates the hidden power behind user generated pictures and their information (i.e. pictures information and/or users’ information) leading to the suggestion of a possible new touristic experience without knowing anything of the travelers but a single (or a collection) of pictures. Therefore the research questions are: RQ1: Is it possible to use User Generated Pictures to find similar destination?RQ2: What information is more relevant to determine similar destinations?RQ2.1: Is information about pictures more relevant to determine similar destinations?RQ2.2: Is information about users more relevant to determine similar destinations?RQ3: Is it possible to suggest to visit a destinations based on personal pictures?
Flickr.com easily allows users to get the top tags for a given location (specified as a WOEID) through its flickr.places.tagsForPlace API. However, this API only returns the top 100 unique tags, without any information about the photos taken or the users who uploaded them. For this reason, two distinct datasets were built: The former, called Top100, contains the tags retrieved using the aforementioned API; the latter, called Random, contains a random sampling of photo metadata obtained by querying Flickr APIs with YQL (Yahoo Query Language: an expressive SQL-like language that lets developers query, filter, and join data across Web services.) Selecting, for each city, 10 photos from 300 random days, taken at random hours, avoids bias due to day- or time-related events. An important advantage of this second approach is that user- and photo-related information is available, providing new dimensions across which tag analysis can be performed.
VSM: The VSM (Vector Space Model) was used to represent cities in terms of their related tags. In the VSM each city is represented by a vector in an n-dimensional space, where n is the number of distinct tags, whose components are weighted according to tag frequency.Of course, as tags which are too popular tend to be more widespread and thus less informative, frequency is normalized using the TF-IDF approach, which normalizes a TF (Term Frequency) with an IDF (Inverse Document Frequency) factor. This takes into account the number of documents containing that term (in our case, the number of cities for whose photos a given tag has been used).
Each user judged one (random) city at a time, for a total of twenty distinct cities. For each of them the user was provided results of the four scoring systems, in the form of four lists containing the top-five related cities. After users’ choice of the similarity system the interface posed two questions in order to (i) assess the confidence level of the respondent within the association made (measured on a 1-5 Likert Scale) and assess the willingness of the respondent to recommend the selected cities belonging to the chosen system to someone that visited the main city.In the front end the sections spaces have been allocated to: (1) information about the place taken from Yahoo! GeoPlanet (http://developer.yahoo.com/geo/geoplanet/), which was also used to disambiguate the given city from its homonyms (e.g. in the case or Rome it is possible to have a conflict between Rome in Italy and Rome in Georgia); (2) Map from Yahoo! maps; (3) Instruction on how to complete the survey (a redundancy with the first part of the survey was desired because every users was asked to complete 15 cities); (4) the four systems to be rated (differently from previous experiences the font size was formatted in the same way in order not to influence responders). Since the similarity between two places is based on the similarity of their tag descriptions, users had the possibility to check the tags in common between two cities simply clicking on the name of the city. This is clearly a simplification, as the adopted similarity metrics were not based on a simple term match. It was considered useful to provide a rough idea of why two cities had been considered similar.
The best system according to users (Figure 2) is system C (n=92 preferences). System C was the random dataset based on picture information (IDFP). Then system B (n=66 preferences) the random dataset, standard IDF along with System D (n=63 preferences) the random dataset based on picture users (IDFU). The last system was system A (n=53 preferences) created with knowledge gathered by the Top100 dataset. Non valid answers (N) were only 22.37,5 % of the choices have been given with an high or fairly good degree of confidence (Likert scale 4 and 5) , while 34,8% of the choices have been selected with a low or fairly low degree of confidence (Likert scale 1 and 2).
Furthermore, it was asked to the users if they were willing to recommend the selected cities within the selected system to someone that visited the main city: what is noticeable is that aggregating the average, fairly good and high degree of choice confidence (Likert scale values 3, 4 and 5 – 65,2% of the observations) it is possible to define a degree of recommendation of 56.7% (Figure 3).
Flickr Destinations Similarity
www.bournemouth.ac.ukHarvesting User Generated Picture InformationTo Understand Destination SimilarityDr. Alessandro InversiniSchool of TourismBourbemouth UniversityDr. Davide EynardFaculty of InformaticsUniversitá della Svizzera email@example.com@bournemouth.ac.ukJune 6th 2013
www.bournemouth.ac.ukaimTo understand:the importance of the user generated pictures in understanding the destinationsimilarity in order to lead to a possible recommendation of a destination to visit.pippoRecSysWeb2.0Picture
www.bournemouth.ac.uk…what’s the role of pictures in tourism?
www.bournemouth.ac.ukPictures are essential also for destinations
www.bournemouth.ac.uk• Tourists have technological needs during the all tourism goodsconsumption process• Advancements in technologies have made easier to takepicture & to share pictures.What is happening with technologies and social media?Gretzel et al., 2006
www.bournemouth.ac.ukWeb2.0 & Social Mediai) the web is conceived more as a public square where to connect and exchangeopinions instead of a library;ii) the possibility of publishing contents has been widespread thanks to easy-to-usewebsites and applications;iii) the availability of large bandwidth connections makes possible a wider use ofmultimedia, leading to good quality, interactive content provided by the usersthemselves. (Cantoni and Tardini, 2009).
www.bournemouth.ac.ukWeb2.0 & Social MediaSocial Media are: “media impressions created by consumers, typically informed byrelevant experience, and archived or shared online for easy access by otherimpressionable consumers” (Blackshaw, 2006)They represent “a mixture of fact and opinion, impression and sentiment, foundedand unfounded tidbits, experiences, and even rumor” (Blackshaw & Nazarro, 2006)Social media are important as they help spread within the web the electronic Wordof Mouth. (Litvin, Goldsmith, & Pan, 2008)
www.bournemouth.ac.ukWeb2.0 & Social MediaOne in two tourists view destination’s photos via UGC in different web communitiesYoo and Gretzel (2009).For example to understand culture (Pengiran-Kaha et al., 2010) or to recommenda place to visit (Linanza et al., 2011).According to Xiang and Gretzel (2010) social media are playing a relevant rolewithin travel and tourism search online.Image and video sharing website count for 3.8% (Inversini and Cantoni 2011).
www.bournemouth.ac.ukFlickr.comTags: terms used for describing the pictureGeotags: descrption of the location of the pictureFolksonomies and PersonomiesThe term folksonomy was introduced by Vander Wal(2004), by mixing the terms “folk” and “taxonomy”.Users assign a set of terms called tags to anindividual piece of content in order to group orclassify it for retrieval (Sturtz, 2004).The collection of the tags of a single user is calledpersonomy, while the collection of personomies iscalled folksonomy (Hotho et al., 2006)
www.bournemouth.ac.ukRecommendation SystemCollaborative filtering, which aggregates data about user preferences (i.e. ratings) to recommendnew products. In the specific case of tourism destinations, this would require users to (i) visit adestination and (ii) explicitly provide a rating for it.Content based filtering (Pazzani & Billsus 2007) mainly exploits user preferences (implicit orexplicit) to build a model of user’s interests. For the recommendation of tourism destinations,this would require users to express their preferences (either by booking flights or rooms indifferent destinations or by explicitly “liking” them). Moreover, a representation of destinationsrich enough to distinguish between what the user liked and what she did not would be necessary.The knowledge-based approach uses knowledge about users and theapplication domain to reason over product similarity and choose whichones to recommend. In the field of tourism, this would mean finding ametric which exploits external knowledge to define similarity betweendestinations.(Lorenzi, Ricci, Tostes and Brasil, 2005)
www.bournemouth.ac.ukSo??What the importance of the user generated pictures in understanding the destinationsimilarity in order to lead to a possible recommendation of a destination to visit?
www.bournemouth.ac.uk• Harvest 233 cities in Flickr.com*– Each city was represented by the collection of all the tags assigned to itspictures– Information about users (who upload a the given picture)– Information about the pictures (picture sharing the same tag)– Geotags: harvested and used to disambiguate• 4 sets of data– Top 100 tags (Flickr API picture only tags – System A)– Top 100 tags (Flickr API users information – System B)– “Random tags”(YQL only picture tags – System C)– “Random tags” (YQL users information – System D)method*http://www.euromonitor.com/Top_150_City_Destinations_London_Leads_the_Way (2007 and 2008)
www.bournemouth.ac.uk• Vector Space Model was used to represent thecities in terms of their tags.• Normalize sets (e.g.)• Calculate similaritysimilarity(a, b)= cos(a) =a×ba bIDFUi, j=PjPi, jUi, jUjmethod
www.bournemouth.ac.ukmethod• Submit sets to a sample of users51 users296 valid observations- 47% 25-30 years old- 45% italian- 50.9% expert travellers** travelled 5-10 times the previous year
www.bournemouth.ac.ukResults• System C was the more reliable for users• 37,5 % choices given with of confidence– Highest level of confidence for system C– Lowest level of confidence for system A
www.bournemouth.ac.ukResults• Willingness to recommend a city
www.bournemouth.ac.ukDiscussion & Conclusion• It is possible to define similar destinations on the basis ofpictures images tags.• Flickr APIs are not enough for defining destinations’similarity (SystemC vs SystemA)BUT• Information about pictures are enough for definingdestination similarity.• IT IS THEREFORE POSSIBLE to recommend destinationson the basis of the pictures uploaded on social media.
www.bournemouth.ac.ukHarvesting User Generated Picture InformationTo Understand Destination SimilarityDr. Alessandro InversiniSchool of TourismBourbemouth UniversityDr. Davide EynardFaculty of InformaticsUniversitá della Svizzera firstname.lastname@example.org@bournemouth.ac.ukMay 22nd 2013
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