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Semantically Enhancing Recommender Systems

  • Nuno Bettencourt
  • Nuno Silva
  • João Barroso
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 631)

Abstract

As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain’s specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource’s content, user’s preferences, users’ social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources’ semantic information. The recommendation engine allows the promotion and discovery of unknown-unknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources.

Keywords

Recommendation Access policy Unknown-Unknown 

Notes

Acknowledgements

This work is supported through FEDER Funds, by “Programa Operacional Factores de Competitividade - COMPETE” program and by National Funds through “Fundaçãopara a Ciência e a Tecnologia (FCT)” under the project Ambient Assisted Living for All (AAL4ALL – QREN 13852).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.GECAD and Instituto Superior de Engenharia, Instituto Politécnico do PortoPortoPortugal
  2. 2.INESC TEC and Universidade de Trás-os-Montes e Alto DouroVila RealPortugal

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