Introduction Proposed System Use case Conclusions Acknowledgements

An Aspect Based Resource RecommendationSystem for Smart Hotels

Aitor Almeida1, Eduardo Castillejo1, Diego Lopez-de-Ipina1,Marcos Sacristan2 and Javier Diego3

1DeustoTech - Deusto Institute of Technology, University of Deusto http://www.morelab.deusto.es

2Treelogic http://www.treelogic.com

3Logica http://www.logica.com/es

September 24, 2012

Introduction Proposed System Use case Conclusions Acknowledgements

Index

1 IntroductionProblemProposed solution

2 Proposed SystemResourcesAspectsSuitability

3 Use case

4 ConclusionsConclusionsFuture work

5 Acknowledgements

Introduction Proposed System Use case Conclusions Acknowledgements

Problem

Introduction

The number of resources available in a Smart Environmentcan be overwhelming.

Using user and resource features with context data canhelp in the recommendation filtering process.

Our domain: Smart hotelsA proactive domain which makes recommendations to its users.It must know about users’ preferences, tastes and limitationsor capabilities.It must be capable of analysing the different aspects thatdefine a resource to offer the most appropriate one to the user.

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 beused to describe it in a Smart Environment.

These aspects take into account both user and resourcefeatures and the current context.

Introduction Proposed System Use case Conclusions Acknowledgements

Resources

Proposed System

Resource type (in our domain):

Pyshical serviceVirtual serviceMultimedia contentOthe information (maps, news...)

Defined aspects must be generic enough to be used todescribe all the available resources. In the currentimplementation we have considered:

1. Predictability2. Accessibility3. Relevancy4. Offensiveness

Introduction Proposed System Use case Conclusions Acknowledgements

Resources

Proposed System

Resource type (in our domain):

Pyshical serviceVirtual serviceMultimedia contentOthe information (maps, news...)

Defined aspects must be generic enough to be used todescribe all the available resources. In the currentimplementation we have considered:

1. Predictability2. Accessibility3. Relevancy4. Offensiveness

Introduction Proposed System Use case Conclusions Acknowledgements

Resources

Proposed System

Resource type (in our domain):

Pyshical serviceVirtual serviceMultimedia contentOthe information (maps, news...)

Defined aspects must be generic enough to be used todescribe all the available resources. In the currentimplementation we have considered:

1. Predictability2. Accessibility3. Relevancy4. Offensiveness

Introduction Proposed System Use case Conclusions Acknowledgements

Aspects

Proposed System

1. PredictabilityIt reflects how likely a resource is to be used based on theresources previously consumed.The likeliness is expressed as a probability value 0..1Markov Chains to create the model of the user’s resourceusage → ascertain patterns in the user behaviour

One of the Markov Chains created with theresource consumption data for thepredictability aspect. Using the created modelthe recommender system can predict thelikeness of one resource to be the next to beconsumed

Introduction Proposed System Use case Conclusions Acknowledgements

Aspects

Proposed System

2. AccessibilityUsers possess a wide variety of abilities (sensorial, cognitiveand so on) that must be taken into account to asses thesuitability of the resources.

Taxonomy of the user abilities taken into account in the accessibility aspect. Disabilities areclassified in three categories.

Users must be able of consuming every resource.

Introduction Proposed System Use case Conclusions Acknowledgements

Aspects

Proposed System

Each resource has:

required user abilitiesrecommended 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 bythe user.

Introduction Proposed System Use case Conclusions Acknowledgements

Aspects

Proposed System

3. RelevancyIt measures the importance of a resource to the user’scurrent context.Context variables:

User locationTime of the dayCurrent 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.

Introduction Proposed System Use case Conclusions Acknowledgements

Aspects

Proposed System

4. OffensivenessIt measures the suitability of a resource based on a ratingsystem.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, takingthe age categories as required constraints and the contentdescriptions as the recommended ones.

Introduction Proposed System Use case Conclusions Acknowledgements

Suitability

Proposed System

Suitability: It is a dynamic and personalized value for anaspect to a specific user:

Mtot = Σwifi

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 arenormalized.

Introduction Proposed System Use case Conclusions Acknowledgements

Use case

Two different users (in their rooms, the service R1 has justbeen 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 = 1Accessibility and Offensiveness = 0.5

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× 0.7Recommended resource: R2

Introduction Proposed System Use case Conclusions Acknowledgements

User 2

Content restriction (Press digest has a minimun age categoryof 7) → scoring 0 in Offensiveness

No disability.

Using the suitability formula:Mtot = 1× 0.5 + 0.5× 1 + 0.5× 1 + 1× 0.9Recommended resource: R4

Introduction Proposed System Use case Conclusions Acknowledgements

Conclusions

Conclusions

AdvantagesApplicable to all the resource types identified in an intelligenthotel domain: digital and physical services, multimedia contentand data.Configurable process (weights).Creation of a comprehensive picture of the current situation torecommend the most suitable resource.Anticipation of future user needs.

LimitationsWith Markov Chains we evaluate Predictability, but we don’tevaluate the previous events that preceded the current one...(Time Series?)More aspects are needed.

Introduction Proposed System Use case Conclusions Acknowledgements

Future work

Conclusions

Aspect we are working on:

Timeliness: it evaluates how up to date is the informationabout a resource.Satisfaction: measures the opinion of the users about aresource.Attention: The average number of interactions per time unitwith a consumed resource.Closeness: Evaluates what resources are consumed by similarusers.

We aim to include vagueness and uncertainty in the contextdata information by ambiguity assessing techniques.

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 DesarrolloTecnologico Industrial (CDTI) and supported by the SpanishMinisterio de Ciencia e Innovacion.