1. Introduction Proposed System Use case Conclusions Acknowledgements
An Aspect Based Resource Recommendation
System for Smart Hotels
Aitor Almeida1 , Eduardo Castillejo 1 , Diego L´pez-de-Ipi˜a1 ,
o n
Marcos Sacrist´n
a 2 and Javier Diego3
1 DeustoTech - Deusto Institute of Technology, University of Deusto http://www.morelab.deusto.es
2
Treelogic http://www.treelogic.com
3
Logica http://www.logica.com/es
September 24, 2012

2. Introduction Proposed System Use case Conclusions Acknowledgements
Index
1 Introduction
Problem
Proposed solution
2 Proposed System
Resources
Aspects
Suitability
3 Use case
4 Conclusions
Conclusions
Future work
5 Acknowledgements

3. Introduction Proposed System Use case Conclusions Acknowledgements
Problem
Introduction
The number of resources available in a Smart Environment
can be overwhelming.
Using user and resource features with context data can
help in the recommendation filtering process.
Our domain: Smart hotels
A proactive domain which makes recommendations to its users.
It must know about users’ preferences, tastes and limitations
or capabilities.
It must be capable of analysing the different aspects that
define a resource to offer the most appropriate one to the user.

4. Introduction Proposed System Use case Conclusions Acknowledgements
Proposed solution
Introduction
An aspect-based resource recommendation system.
We have identified the aspects of a resource that can be
used to describe it in a Smart Environment.
These aspects take into account both user and resource
features and the current context.

5. Introduction Proposed System Use case Conclusions Acknowledgements
Resources
Proposed System

6. Introduction Proposed System Use case Conclusions Acknowledgements
Resources
Proposed System
Resource type (in our domain):
Pyshical service
Virtual service
Multimedia content
Othe information (maps, news...)

7. Introduction Proposed System Use case Conclusions Acknowledgements
Resources
Proposed System
Resource type (in our domain):
Pyshical service
Virtual service
Multimedia content
Othe information (maps, news...)
Defined aspects must be generic enough to be used to
describe all the available resources. In the current
implementation we have considered:
1. Predictability
2. Accessibility
3. Relevancy
4. Offensiveness

8. Introduction Proposed System Use case Conclusions Acknowledgements
Aspects
Proposed System
1. Predictability
It reflects how likely a resource is to be used based on the
resources previously consumed.
The likeliness is expressed as a probability value 0..1
Markov Chains to create the model of the user’s resource
usage → ascertain patterns in the user behaviour
One of the Markov Chains created with the
resource consumption data for the
predictability aspect. Using the created model
the recommender system can predict the
likeness of one resource to be the next to be
consumed

9. Introduction Proposed System Use case Conclusions Acknowledgements
Aspects
Proposed System
2. Accessibility
Users possess a wide variety of abilities (sensorial, cognitive
and so on) that must be taken into account to asses the
suitability of the resources.
Taxonomy of the user abilities taken into account in the accessibility aspect. Disabilities are
classified in three categories.
Users must be able of consuming every resource.

10. Introduction Proposed System Use case Conclusions Acknowledgements
Aspects
Proposed System
Each resource has:
required user abilities
recommended user abilities
We penalize resources that can not be consumed by the user:
Aacc = 1 − ω|Recnot |
Aacc is the value of the accessibility for the resource.
ω is the penalization weight.
|Recnot | is the number of recommended abilities not met by
the user.

11. Introduction Proposed System Use case Conclusions Acknowledgements
Aspects
Proposed System
3. Relevancy
It measures the importance of a resource to the user’s
current context.
Context variables:
User location
Time of the day
Current activity: sleeping, hygiene routine, eating, exercising,
working, shopping and visiting tourist attractions.
For the classifier we have used KNN (k-nearest neighbor)
supervised classification method.

12. Introduction Proposed System Use case Conclusions Acknowledgements
Aspects
Proposed System
4. Offensiveness
It measures the suitability of a resource based on a rating
system.
We use the age categories and content descriptions by PEGI
(Pan European Game Information) rating system.
To evaluate it, we use the same Accessibility formula, taking
the age categories as required constraints and the content
descriptions as the recommended ones.

13. Introduction Proposed System Use case Conclusions Acknowledgements
Suitability
Proposed System
Suitability: It is a dynamic and personalized value for an
aspect to a specific user:
Mtot = Σwi fi
Mtot is the value of the suitability of each resource.
wi is the weight for an aspect.
fi is the value of the aspect of a resource. These values are
normalized.

14. Introduction Proposed System Use case Conclusions Acknowledgements
Use case
Two different users (in their rooms, the service R1 has just
been activated):
User 1: a 27 years old male with a hearing impairment.
User 2: a 6 years old child.
Available resources:
R1: Wake up service.
R2: Room service.
R3: Press digest.
R4: Multimedia system.
R5: Transport service.
Weigths:
Predictability and Relevancy = 1
Accessibility and Offensiveness = 0.5

15. Introduction Proposed System Use case Conclusions Acknowledgements
User 1
No content restriction.
Hearing impairment.
R1 and R4 offer alternative means to use them.
Using the suitability formula:
Mtot = 1 × 0.6 + 0.5 × 1 + 0.5 × 1 + 1 × 0.7
Recommended resource: R2

16. Introduction Proposed System Use case Conclusions Acknowledgements
User 2
Content restriction (Press digest has a minimun age category
of 7) → scoring 0 in Offensiveness
No disability.
Using the suitability formula:
Mtot = 1 × 0.5 + 0.5 × 1 + 0.5 × 1 + 1 × 0.9
Recommended resource: R4

17. Introduction Proposed System Use case Conclusions Acknowledgements
Conclusions
Conclusions
Advantages
Applicable to all the resource types identified in an intelligent
hotel domain: digital and physical services, multimedia content
and data.
Configurable process (weights).
Creation of a comprehensive picture of the current situation to
recommend the most suitable resource.
Anticipation of future user needs.
Limitations
With Markov Chains we evaluate Predictability, but we don’t
evaluate the previous events that preceded the current one...
(Time Series?)
More aspects are needed.

18. Introduction Proposed System Use case Conclusions Acknowledgements
Future work
Conclusions
Aspect we are working on:
Timeliness: it evaluates how up to date is the information
about a resource.
Satisfaction: measures the opinion of the users about a
resource.
Attention: The average number of interactions per time unit
with a consumed resource.
Closeness: Evaluates what resources are consumed by similar
users.
We aim to include vagueness and uncertainty in the context
data information by ambiguity assessing techniques.

19. Introduction Proposed System Use case Conclusions Acknowledgements
Acknowledgements
This work has been supported by project grant CEN-20101019
(THOFU), funded by the Spanish Centro para el Desarrollo
Tecnol´gico Industrial (CDTI) and supported by the Spanish
o
Ministerio de Ciencia e Innovaci´n.
o