personal data and user modelling in tourism

1ENTER 2013, Innsbruck

Personal Data and User Modellingin Tourism

Ioannis Stavrakantonakis

STI Innsbruck University of Innsbruck, Austria

2Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck

Data, data.. more data!

©Google, http://www.google.com/about/datacenters


Social Web data• Facebook: One billion monthly active users,

(https://www.facebook.com/facebook, October 2012)

• Twitter: Summer Olympics ‘12 in London generated 150 million Tweets (https://2012.twitter.com/en/pulse-of-the-planet.html)

• Foursquare: A half billion check-ins the last 3 months, (http://blog.foursquare.com, Jan 17th 2013)


Recommendation systems

Where should you eat for dinner tonight?

What should you visit in Innsbruck?

Where to go for a drink?


Recommenders examples Nara.me asks user’s taste about:• types of restaurants• cuisines• location • 2 restaurants

in the city


SUPE by Toyota[1]:

• In-vehicle navigation system recommender

• Collects driver preferences to provide personalised POI search results to the driver

• Uses GPS logs (historical data)

Recommenders examples (cont.)


The ProblemPersonal Data in Social Web• Data is contained within

disparate silos

Recommendation systems• User models are trapped in

proprietary data warehouses• User model properties are not standardised

in various domains [4] *http://www.economist.com/business/displaystory.cfm?story_id=10880936

Everywhere and nowhere, David Simonds, Economist 2008*


Research Questions

• How could we bring closer the personal data of the users and the recommendation systems?

• How could we lower the borders among the recommenders?

• Which personal data could be used by the recommenders in tourism?


Our Approach

• Open User Model– Capturing personal data from the Social Web– Specific for tourism– Enable both personalisation systems and

travellers to benefit– Based on existing ontologies reuse


Our Approach (cont.)

• Aims to– facilitate the extraction of personal data from

Social Web;– facilitate the interoperability among

recommenders in the tourism domain; – enable the users to consume personalised

services from the data that they have already shared in the Social Web.


Related work in user modelling

GUMO [6], SWUM[5]:– Cover any attribute of a user model for the

Social Web– Not specific for any domain– Aim to allow an easy data sharing between

applications

Mypes[3]: – Cross-system user modelling


The methodology

• Define the attributes of the user model. [2]– Basic user characteristics– Interests– Time dimension– Historical data (e.g. visited places)– User’s wishes


The methodology (cont.)

• Following a bottom-up methodology1. study the specifications of social networks

(i.e. Facebook & Foursquare)

2. extract user attributes related to tourism from the data models

3. map the extracted attributes


User Model for TourismUser model aspects Facebook Foursquare Comments

Personal information Name, EmailMarital status Spouse, ChildrenHometown Current city

Visited POIs Coordinates, Name, Category

POIs to Explore POIs saved in ToDo lists

Interests Liked locations Activities


User Model for Tourism (cont.)

• Reuse of existing vocabularies– FOAF (http://xmlns.com/foaf/spec/)

• describe basic information about people • describe Internet accounts, web-based activities

– Geo (http://www.w3.org/2003/01/geo/)• information about spatially-located things

– Wi (http://xmlns.notu.be/wi/)• describe that a person prefers one thing to another


User Model for Tourism (cont.)

umt:hasCurrentLocation

umt:hasHometown

wi:preference

umt:hasVisited

umt:hasToDo

foaf:knows

foaf:Person

foaf:namefoaf:mboxfoaf:account

umt:POI

umt:nameumt:categoryumt:timestampgeo:latgeo:long

wi:WeightedInterest

Property

Subclass of

umt:Location

umt:namegeo:latgeo:long

umt:likesLocation


Conclusion• The data models of Social Networks are

very similar regarding the visited places of the users.

• Personal data in the Social Web contain reusable information for recommendation in the tourism domain.

• An approach for the exploitation of this data in tourism.


Future steps

• Finalisation of the UMT model

• Exploitation of the Google Latitude data

• Evaluation of the approach and model


Questions?

[email protected]@istavrak


References1. Parundekar, R., & Oguchi, K. (2012). Learning Driver Preferences of POIs Using

a Semantic Web Knowledge System. The Semantic Web: Research and Applications.

2. Kang, E., Kim, H., & Cho, J. (2006). Personalization method for tourist point of interest (POI) recommendation. Knowledge-Based Intelligent Information and Engineering Systems.

3. Abel, F., Herder, E., Houben, G., Henze, N., & Krause, D. (2011). Cross-system user modeling and personalization on the social web. UMUAI Journal.

4. Aroyo, L., & Houben, G. (2010). User modeling and adaptive Semantic Web. Semantic Web Journal.

5. Plumbaum, T., Wu, S., De Luca, E., & Albayrak, S. (2011). User Modeling for the Social Semantic Web. Proceedings of SPIM 2011.

6. Heckmann, D., Schwarzkopf, E., Mori, J., Dengler, D., & Kröner, A. (2007). The user model and context ontology GUMO revisited for future Web 2.0 extensions. Contexts and Ontologies: Representation and Reasoning.

personal data and user modelling in tourism

Technology

problempersonal data

city personal data

user models

user modellinggumo

social web data facebook

tourism domain

crosssystem user

approach open user model