An Aspect Based Resource Recommendation System for Smart Hotels
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An Aspect Based Resource Recommendation System for Smart Hotels Presentation Transcript

  • 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 AcknowledgementsIndex 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 AcknowledgementsProblemIntroduction 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 AcknowledgementsProposed solutionIntroduction 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 AcknowledgementsResourcesProposed System
  • 6. Introduction Proposed System Use case Conclusions AcknowledgementsResourcesProposed System Resource type (in our domain): Pyshical service Virtual service Multimedia content Othe information (maps, news...)
  • 7. Introduction Proposed System Use case Conclusions AcknowledgementsResourcesProposed 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 AcknowledgementsAspectsProposed 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 AcknowledgementsAspectsProposed 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 AcknowledgementsAspectsProposed 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 AcknowledgementsAspectsProposed 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 AcknowledgementsAspectsProposed 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 AcknowledgementsSuitabilityProposed 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 AcknowledgementsUse 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 AcknowledgementsUser 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 AcknowledgementsUser 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 AcknowledgementsConclusionsConclusions 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 AcknowledgementsFuture workConclusions 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 AcknowledgementsAcknowledgements 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