Future Research Issues in IT and Tourism

Francesco Ricci Free University of Bozen-Bolzano

Italy fricci@unibz.it

ICT & Tourism Business Initiative

p  The Internet has changed the way consumers plan and buy their holidays and how tourism providers design, shape, promote and sell their products and services

p  The tourism market relies heavily on information p  Tourism demand shifted from mass tourism to customized

tourism for individual travellers p  Big tourism companies have responded to the opportunities

offered by the Internet and developed their own ecommerce applications

p  Most SMEs in the tourism sector have traditionally avoided the rather costly electronic distribution networks and have established their own Internet presence for advertising products and services.

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http://ec.europa.eu/enterprise/sectors/tourism/ict/index_en.htm

Challenges – fragmentation and heterogeneity

p  from TOURISMLink, P. Marone n  Distribution very fragmented n  Heterogeneous consumers n  SMEs are not integrated in the digital supply

chain n  Insufficient adoption of e-integrated businesses

processes n  Variety of different tourism information

systems, with different areas of operation and technologies

n  Data heterogeneity and interoperability gaps. 3

Challenges II

p  From AMADEUS

n  Optimize distribution capabilities across all relevant channels

n  Become visible (efficient marketing) and relevant (personalized your offer) in an increasingly complex online environment

n  Enrich the travel experience of your customer n  Stay connected to your customer/traveler from

travel planning phase to post-trip n  Do all the above efficiently.

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Summary

A seamless network of heterogeneous suppliers can offer better services to traveller

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Examples

p  Better exploitation of (open) data p  Better exploitation of the situational knowledge p  Better understanding user's personality p  Proactivity p  Better helping users to understand their

preferences p  Better help users to

understand the consequences of their choices

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STS video

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Issues – commented I

p  Better exploitation of (open) data n  Mashup of meteo data (Mondometeo), attractions

(Regional Association of South Tyrol’s Tourism Organizations), city services (Municipality of Bolzano), Parking data (TIS), Bus data (SASA), routing (Google transit)

p  Better exploitation of the situational knowledge n  Contextual dependent preferences acquired and used for

generating more relevant suggestions (context-aware matrix factorization)

p  Better understanding user's personality n  User personality can be used to bootstrap the human/

computer interaction model when no much additional user data are available. 8

Issues – commented II

p  Proactivity n  Querying the user by guessing what information the

user can provide and may be also useful for other users (Active Learning)

p  Better helping users to understand their preferences n  Explanations of why an item may be interesting, given

the user current situation n  Let them specify the context of the experience

p  Better help users to understand the consequences of their choices n  Showing the situation of the POI when the user will be

at destination (e.g., no parking will be available). 9

Back to the future

p  Provide meaningful replies/suggestions along the full history of the traveller

p  Better understand how preferences are constructed – the role of memory

p  Enable and support groups of travellers to plan and experience a travel together

p  Go beyond the prediction of what the user will like: suggest what the user need

p  Tourists decision making is influenced primarily by System 1 – not only by System 2.

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Sequential Recommendation

p  Recommendations at time t depends on user reactions to suggestions at time t-k

p  Example: music played by your player now, depends on the time spent listening to previously played tracks

p  Example: destinations suggested for the next summer holidays depends on the places you visited in recent years.

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Omar Moling, Linas Baltrunas, Francesco Ricci: Optimal radio channel recommendations with explicit and implicit feedback. RecSys 2012: 75-82

Remembering The Stars?

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Rating as function of time past after watching a movie. Dashed line for initially high rated movies, solid line for initially low rated movies.

The movies were split based on the average rating in the first three timeslots to see if ratings for good and bad movies change differently with time. Over time ratings regress to the middle of the scale.

Dirk G. F. M. Bollen, Mark P. Graus, Martijn C. Willemsen: Remembering the stars?: effect of time on preference retrieval from memory. RecSys 2012: 217-220

Multiparty Recommendation

Cri$quing   Cri$quing  

cuisine  atmosphere  price  

Da  Gennaro  

cuisine  atmosphere  price  

“I  prefer  a  hipper  atmosphere”  …  8^)  

Che  Maxim  

Francesca Guzzi, Francesco Ricci, Robin D. Burke: Interactive multi-party critiquing for group recommendation. RecSys 2011: 265-268

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Budgeted Social Choice

p  Example: your brochure can list a maximum of ten services that jointly maximize consumer satisfaction

p  Select the combination of services such that the sum of the satisfaction of the users, when they select the best option for them, is maximized

p  Comparison n  Recommender Systems: one option one user n  Group Recommender Systems: one option

many users n  Budgeted Social Choice: many options many

users 15 Tyler Lu, Craig Boutilier: Budgeted Social Choice: From Consensus to

Personalized Decision Making. IJCAI 2011: 280-286

Guess the Utility

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Utility = Happiness – Cost + Health Utility(Holiday) = Like(Holiday) – Cost(Holiday) + Healthiness(Holiday) Current advisory systems models and predict only to what extent you like something (ratings).

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System1 and System2

p  Psychologists [Stanovich and West] claim that two systems are operating in the mind:

p  System 1: operates automatically and quickly, with little or no effort and no sense of voluntary control

p  System 2: allocates attention to the effortful mental activities that demand it, including complex computations.

p 17 x 24 = 17 D. Kahneman, Thinking, fast and slow, Allen Lane pub., 2011

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System 1 and System 2 p  Users select options by

estimating their expected utility n  This is influenced by

the context (interaction) n  System 1 is sometimes

is deciding for you

p  Recommender systems predict your behavior based on remembered utility and adopt System 2 logic n  Users may show completely different

behaviors from those predicted! 18

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