Information and
Communication
Technologies
in
Tourism
Recommendation and Planning Systems for
Tourism Products and Services
Recommender Systems in Tourism
Interaction strategies
2
Definition and classification
Search interfaces
Collaborative browsing
Future issues
ICT in Tourism
© copyright IFITT
Recommendation and Planning Systems for
Tourism Products and Services
Recommender Systems in Tourism
Interaction strategies
3
Definition and classification
Search interfaces
Collaborative browsing
Future issues
ICT in Tourism
© copyright IFITT
Recommender systems
4
Recommender systems
Recommend information or products & services
Support customers‘ decision making process
Map customer needs and constraints on product
selections, using knowledge-based methods
Recommend information or products based on
customer profiles (preferences and feedback)
Recommend products similar to products the
customer liked in the past (content-based filtering)
Recommend products similar customers liked in the
past (collaborative filtering)
ICT in Tourism
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Recommender systems
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Objectives of recommender systems
Avoid information overload by intelligent filtering
of information or products & services
Help to make choices without sufficient personal
experience of the alternatives
Reduce cognitive effort and minimise user
interaction
Integrate intelligent behaviour of travel agents
into online platforms
Optimise match between customer needs and
selected products -> optimise customer
satisfaction
ICT in Tourism
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Recommender systems
Examples found in the Web
Amazon.com – looks in the user’s past buying
history and recommends products bought by a
user with similar buying behavior
Tripadvisor.com - Quoting product reviews of a
community of users
ActiveBuyersGuide.com – make questions about
searched benefits to reduce the number of
candidate products
Technologies
6
Information filtering, machine learning, adaptive
and personalized systems, user modeling, …
ICT in Tourism
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Amazon.com
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Amazon.com
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ICT in Tourism
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Amazon.com
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Tripadvisor.com
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Tripadvisor.com
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ActiveBuyersGuide.com
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Virtual Adviser
Jannach, D., Zanker, M. & Fuchs, M. (2009): Constraint-based Recommendation in Tourism: A Multi-Perspective Case Study.
Information Technology and Tourism, 11 (2): 139-155.
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ICT in Tourism
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Virtual Adviser
Form-based dialogues for preference elicitation
Personalized
Recommendation
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ICT in Tourism
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Virtual Adviser
Business Intelligence by mining log file data
χ2(1) = 62.87; p < 0.000
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Systems in Tourism:
A Web-Usage Mining Approach, In:
O’Connor et al. (eds.), Information and Communication Technologies in Tourism 2008, Springer, NY: 24-34
ICT2008):
in Tourism
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Zanker et al.
Evaluating Recommender
Simplified recommendation model
Two types of entities: Users and Items
Background knowledge
A set of features of the users and/or items
A set of ratings
?}
A method for eliminating all or part of the
‘?’ values for user-item pairs – substituting
‘?’ with true values
A method for selecting the items to
recommend
Recommend to u the item i* such that
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R: Users x Items {1, 2, 3, 4, 5,
ICT in Tourism
i* = maxi∈Items {R(u,i)}
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Adomavicius et al. 2005
Simplified recommendation model
i1
i2
5
u1
User
features
ratings
u2
?
Product
features
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ICT in Tourism
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Recommendation techniques
U is a set of users
I is a set of items/products
Burke 2002
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Collaborative filtering
Current user
Dislike
1
Like
?
Unknown
User model =
interaction
history
1st item rate
Items
0
1
?
1
0
1
1
0
1
1
0
1
1
1
1
0
14th item rate
Hamming distance 5
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ICT in Tourism
Users
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6
6
5
4 8
Nearest neighbor
Collaborative filtering
A collection of users ui, i=1, …n and a collection of
products pj, j=1, …, m
A n × m matrix of rates vij , with vij = ? if user i did not rate
product j
Prediction is computed as
v*ij = vi + K∑v ≠? uik (vkj −vk )
kj
where, vi is the average rate of user i, K is a normalization factor
such that the sum of uik is 1, and
∑ (v
ij
uik =
− vi )(vkj − vk )
j
2
2
(
v
−
v
)
(
v
−
v
)
∑ j ij i ∑ j kj k
Similarity of
users i and k
where the sum is over all j where vij and vkj are not “?”
Adomavicius et al. 2005
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ICT in Tourism
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Collaborative filtering
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Pros
Require minimal knowledge engineering efforts
Users and products are symbols without any internal
structure or characteristics
Cons
Requires a large number of explicit and reliable “rates”
to bootstrap (new user problem & new item problem)
Requires products to be standardized (users should
have bought exactly the same product)
Does not provide information about products or
explanations for the recommendations
Does not support sequential decision making or
recommendation of “good bundling”, e.g., a travel
package
ICT in Tourism
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Content-based filtering
Item attributes
User
Dislike
1
Like
?
Unknown
ICT in Tourism
1st item rate
Items
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0
1
?
1
0
1
1
0
1
1
0
1
1
1
1
0
14th item rate
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Content-based filtering
A collection of products pj, j=1, …, m and
product descriptions Content(pj) = (a1j,…,akj)
A user profile ContentBasedProfile(u) = (a1u,…,aku) containing
tastes and preferences of the user
Prediction is computed as
vuj = score(ContentBasedProfile(u), Content(pj))
where score is usually defined as scoring heuristic
such as the cosine similarity measure or
Bayesian classifiers
Adomavicius et al. 2005
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Content-based filtering
Pros
Cons
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Provides explanations of recommendations and
relevant product information
New items can be recommended without specific
„rates“
Requires a large number of explicit “rates” to
bootstrap (new user problem)
Recommendation only suggests items similar to
items rated or used in the past
(overspecialization)
Recommendation is limited by the features
explicitly associated with the items/products
ICT in Tourism
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Case-based reasoning
Ricci et al. 2005
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ICT in Tourism
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Case-based reasoning
Ricci et al. 2005
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ICT in Tourism
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Travel advisory requirements
Recommendation process
Input/output
Products and services have complex structures
The final recommendation is a bundling of elementary
components
Allow system bootstrapping without an initial memory of rating
interactions
Generalize the definition of ratings (implicit ratings)
Users
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Recommendation requires information seeking – not only
filtering
Human/computer interaction should be supported – e.g. user
criticizes a suggested product or refines a query definition
Both short term (goal oriented) preferences and long term
(stable) preferences must influence the recommendation
Unregistered users should be allowed to get recommendations
Structure/language of users’ needs and wishes may not match
those of the products
ICT in Tourism
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NutKing
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ICT in Tourism
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NutKing
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ICT in Tourism
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NutKing
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ICT in Tourism
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Recommendation and Planning Systems for
Tourism Products and Services
Recommender Systems in Tourism
Interaction strategies
31
Definition and classification
Search interfaces
Collaborative browsing
Future issues
ICT in Tourism
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Attribute-based search interfaces
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ICT in Tourism
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Attribute-based search interfaces
Attribute-based search may fail because
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The user has to translate his vague wish into a
precise query and the cognitive effort for the user
is high
The user does not have knowledge of the tourism
jargon that is typically used in the description of
travel products and services
The user can be intimidated and even not able to
use advanced search tools based on queries –
conjunction of constraints
The user is seeking suggestions, hints, and
inspiration rather than options that must optimize
a collection of decision criteria
ICT in Tourism
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Personality categories
Pre-defined personality
categories
Less cognitive effort
Time savings
Fun
Capturing personality
as well as activities
Difficult validation
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ICT in Tourism
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Personality categories
Traveltypes.com
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ICT in Tourism
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Personality categories
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ICT in Tourism
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Query by example
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ICT in Tourism
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Recommendation and Planning Systems for
Tourism Products and Services
Recommender Systems in Tourism
Interaction strategies
38
Definition and classification
Search interfaces
Collaborative browsing
Future issues
ICT in Tourism
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Collaborative browsing - interface
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ICT in Tourism
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Collaborative browsing - process
Seeking for Inspiration
seed case
Retrieval
Selection
I-Like(ci)
user
(c1, c2, c3 , c4 , c5 , c6)
Case Base
Browsed
Cases
Presentation
Explanation
Ricci et al. 2005
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ICT in Tourism
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Collaborative browsing - process
Seeking for Inspiration
seed case
Retrieval
Selection
I-Like(ci)
user
(c1, c2, c3 , c4 , c5 , c6)
Case Base
Browsed
Cases
Presentation
Explanation
First Retrieval. With a seed case c (the current case or a random case)
the system searches for the M most similar cases in the case base.
Ricci et al. 2005
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ICT in Tourism
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Collaborative browsing - process
Seeking for Inspiration
seed case
Retrieval
Selection
I-Like(ci)
user
(c1, c2, c3 , c4 , c5 , c6)
Case Base
Browsed
Cases
Presentation
Explanation
Case Selection. The M cases retrieved from the memory are analyzed to
select smaller subset of candidates to be presented to the user: minimize
the sum of the similarities between them.
Ricci et al. 2005
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ICT in Tourism
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Collaborative browsing - process
Seeking for Inspiration
seed case
Retrieval
Selection
I-Like(ci)
user
(c1, c2, c3 , c4 , c5 , c6)
Case Base
Browsed
Cases
Presentation
Explanation
Explanation. Identify the attributes that are peculiar to one case and are
not common among the six selected cases.
Ricci et al. 2005
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ICT in Tourism
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Collaborative browsing - process
Seeking for Inspiration
seed case
Retrieval
Selection
I-Like(ci)
user
(c1, c2, c3 , c4 , c5 , c6)
Case Base
Browsed
Cases
Presentation
Explanation
Presentation. Selects some images, taken from the products/services
included in the travel, to illustrate pictorially the case content.
Ricci et al. 2005
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ICT in Tourism
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Collaborative browsing - process
Seeking for Inspiration
seed case
Retrieval
Selection
I-Like(ci)
user
(c1, c2, c3 , c4 , c5 , c6)
Case Base
Browsed
Cases
Presentation
Explanation
User Feedback. The user browses the offers, and eventually to provide a
positive feedback on one of these cases. This feed-back, i.e., the liked
case is given as input for next retrieval.
Ricci et al. 2005
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ICT in Tourism
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Collaborative browsing - process
selected case
Seeking for Inspiration
Retrieval
Selection
I-Like(ci)
user
(c1, c2, c3 , c4 , c5 , c6)
Case Base
Browsed
Cases
Presentation
Explanation
Second retrieval. The procedure described above is repeated, but the
seed case is now the case that received positive feedback, and the number
of cases retrieved from the case base (M) is decrease by a factor 0 < λ < 1.
Ricci et al. 2005
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ICT in Tourism
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Collaborative browsing - process
User’s selection
Initial
case
Ricci et al. 2005
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ICT in Tourism
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Collaborative browsing - process
User’s selection
Ricci et al. 2005
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ICT in Tourism
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Recommendation and Planning Systems for
Tourism Products and Services
Recommender Systems in Tourism
Interaction strategies
49
Definition and classification
Search interfaces
Collaborative browsing
Future issues
ICT in Tourism
© copyright IFITT
Future issues for recommender systems
Comprehensive understanding of users and items
Profiling techniques based on data mining to automatically
build user profiles (web usage mining)
Multi-dimensionality of recommendations
Integration of contextual information (time, season, etc.)
Moving from a two-dimensional user x item space to a multidimensional space D1 x … x Dn
Multi-criteria ratings
Rating several aspects of items instead of rating the item as
a whole (singe-criteria rating)
Nonintrusiveness
Implicit ratings (based on proxies like time spend on
viewing an item, booking an item, etc.) instead of explicit
ratings provided by the user
Flexibility
Customizable recommendations instead of hard-wired
solutions
Aggregated recommendations (travel packages)
Adomavicius et al. 2005
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