Title: Integrating Travel Planning and OnTour Support in a CaseBased Recommender System
1Integrating Travel Planning and On-Tour
Supportin a Case-Based Recommender System
- Francesco Ricci, Dario Cavada and Quang Nhat
Nguyen - eCommerce and TourismResearch Laboratory
- ITC-irst
- http//ectrl.itc.it
Partially supported by Fondazione Cassa di
Risparmio di Trento e Rovereto
2Content
- Application Scenario
- Intelligent Travel Recommender pre travel
component - Objectives of mITR mobile extension
- Usage Scenario of the mobile component
- Query refining based on user feed-back
- Technological infrastructure
- Conclusions
3Application Scenario
- Pre-travel ITR
- User leisure traveler
- Task select a destination for a residential
holiday in Trentino plus an accommodation and
additional attractions. - Output a travel bag containing a set of travel
products (booked or simply chosen) - On-Tour mITR
- When the traveler is On-Tour he/she is interested
in some complementary services that fits well
into the plan. In this case the details like
distance, cost or opening time are very
important.
4ITR (Intelligent Travel Recommender)
General Wishes
Add to travel bag
5Objectives of mITR
- The ITR cannot be simply adapted to a mobile
system. - Develop methodologies and software components for
a mobile application that can extend and
integrate ITR methodologies to support on-tour
travelers. - Methods for interactively refining a user request
exploiting explicit user feedback given to sample
products shown to him and (partially) satisfying
the initial request. - Reduce the time and the cognitive effort required
to successfully complete a recommendation session.
6Usage Scenario
ITR
The Traveler Creates a Travel Plan
Add the Restaurant to the case base
Get Restaurants(userID, location, time)
Requests restaurants
mITR
Shows best candidates
Updating query and re-ranking
Feed-back (critic) one feature
The new ranked list is shown
Stores result to the local bag
Selects a restaurant
mStorage
7Query update cycle
Queryexecution
Logical and similarity query
Result list(ranked items)
User selectone?
Item added to plan
Feedback and query update
New logical and similarity query
8User feedback
Feedback from the user
Result list (ranked list)
Features list of the item selected
9Initial set of candidates
The system performs the logical query (CityTN)
and retrieves the initial set of candidates from
the restaurant catalog. The rank is given by
computing the similarity of the items in the
result set with those contained in past similar
sessions.
Recommender System DB
Result Set
QL(CityTN)
logical query
10Query update
Update the initial query using feed-back from the
user. Initial query (city TN) (?,?,?)
- If the user select the
- I like thai cuisine then(city TN) (? , ? ,
Thai) - I would like a less expensive one(city TN)
(cost 20 - d) (? , ? , ?) - I would accept a more expensive one(city TN)
(cost 20 d) (? , ? , ?)
11Query update example
(CityTN) (?,?,?)
d(x,y) di(x1,y1) dn (xn,yn)
if feature is unknown d 1 If nominal feature
and xi ? yi d 1 If nominal feature and xi
yi d 0 If Numerical feature (xn-yn/range)
di(xi,yi)
Metric used for similarity ranking
(CityTN) (?,?,Thai)
12Related work
- L. M. Ginty and B. Smyth. Deep dialogue vs casual
conversation in recommender systems. In
Recommendation and Personalization in eCommerce,
Proceedings of the AH2002 Workshop, pages 8089,
Malaga, Spain, May, 28th 2002. Universidad de
Malaga.User selects one item among many
simultaneously shown then the selected item is
used for query updating (not applicable on small
screen devices). - H. Shimazu. Expertclerk Navigating shoppers
buying process with the combination of asking and
proposing. In B. N. (Ed.), editor, Proceedings of
the Seventeenth International Joint Conference on
Artificial Intelligence, IJCAI 2001, pages
14431448, Seattle, Washington, USA, August 4-10
2001.User gives feedback to the whole product
and not to the single feature. It may will take
long time to converge to a good solution. - R. Burke. Knowledge-based recommender systems. In
J. E. Daily, A. Kent, and H. Lancour, editors,
Encyclopedia of Library and Information Science,
volume 69. Marcel Dekker, 2000.Very similar
query refining methods Not a mobile application
and not using long term preferences (past cases)
13Infrastructure
14Conclusions
We have shown an integrated recommender system
(pre-travel and on-tour)
- It uses the ITR system for retrieving an initial
set of best candidates. - It adopts a combination of logical and similarity
query for ranking the result. - It tries to minimize the interaction length using
the feed-back from the user for query refining.
15Thanks
Research partially supported by Fondazione
Cassa di Risparmio di Trento e Rovereto