Flickr Destinations Similarity

www.bournemouth.ac.uk

Harvesting User Generated Picture Information To Understand Destination Similarity

Dr. Alessandro Inversini

School of Tourism

Bourbemouth University

Dr. Davide Eynard

Faculty of Informatics

Universitá della Svizzera italiana

linkedin.com/in/inversini

@beanbol

beanbol.com

ainversini@bournemouth.ac.uk

June 6th 2013

http://blogs.bournemouth.ac.uk/etourismlab/

www.ifitt.org

aimTo understand: the importance of the user generated pictures in understanding the destination similarity in order to lead to a possible recommendation of a destination to visit.

RecSys

Web2.0Picture

…what’s the role of pictures in tourism?

www.bournemouth.ac.uk“the tourist gaze”

Pictures are essential also for destinations

• Tourists have technological needs during the all tourism goods consumption process

• Advancements in technologies have made easier to take picture & to share pictures.

What is happening with technologies and social media?

Gretzel et al., 2006

Web2.0 & Social Media

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;

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: “media impressions created by consumers, typically informed by relevant experience, and archived or shared online for easy access by other impressionable consumers” (Blackshaw, 2006)

They represent “a mixture of fact and opinion, impression and sentiment, founded and unfounded tidbits, experiences, and even rumor” (Blackshaw & Nazarro, 2006)

Social media are important as they help spread within the web the electronic Word of Mouth. (Litvin, Goldsmith, & Pan, 2008)

One 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 recommend a place to visit (Linanza et al., 2011).

According to Xiang and Gretzel (2010) social media are playing a relevant role within travel and tourism search online.

Image and video sharing website count for 3.8% (Inversini and Cantoni 2011).

Flickr.com

Tags: terms used for describing the pictureGeotags: descrption of the location of the picture

Folksonomies and Personomies

The term folksonomy was introduced by Vander Wal (2004), by mixing the terms “folk” and “taxonomy”. Users assign a set of terms called tags to an individual piece of content in order to group or classify it for retrieval (Sturtz, 2004).

The collection of the tags of a single user is called personomy, while the collection of personomies is called folksonomy (Hotho et al., 2006)

Recommendation System

Recommendation System Collaborative filtering, which aggregates data about user preferences (i.e. ratings) to recommend new products. In the specific case of tourism destinations, this would require users to (i) visit a destination and (ii) explicitly provide a rating for it.

Content based filtering (Pazzani & Billsus 2007) mainly exploits user preferences (implicit or explicit) 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 in different destinations or by explicitly “liking” them). Moreover, a representation of destinations rich 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 the application domain to reason over product similarity and choose which ones to recommend. In the field of tourism, this would mean finding a metric which exploits external knowledge to define similarity between destinations.

(Lorenzi, Ricci, Tostes and Brasil, 2005)

What the importance of the user generated pictures in understanding the destination similarity in order to lead to a possible recommendation of a destination to visit?

• Harvest 233 cities in Flickr.com*– Each city was represented by the collection of all the tags assigned to its

pictures– 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)

• Vector Space Model was used to represent the cities in terms of their tags.

• Normalize sets (e.g.)

• Calculate similarity

method

method• Submit sets to a sample of users

51 users296 valid observations

- 47% 25-30 years old- 45% italian- 50.9% expert travellers*

* travelled 5-10 times the previous year

Results

• 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

Results

• Willingness to recommend a city

Discussion & Conclusion

• It is possible to define similar destinations on the basis of pictures images tags.

• Flickr APIs are not enough for defining destinations’ similarity (SystemC vs SystemA)

• Information about pictures are enough for defining destination similarity.

• IT IS THEREFORE POSSIBLE to recommend destinations on the basis of the pictures uploaded on social media.

Harvesting User Generated Picture Information To Understand Destination Similarity

Dr. Alessandro Inversini

School of Tourism

Bourbemouth University

Dr. Davide Eynard

Faculty of Informatics

Universitá della Svizzera italiana

linkedin.com/in/inversini

@beanbol

beanbol.com

ainversini@bournemouth.ac.uk

May 22nd 2013

Flickr Destinations Similarity

Technology

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