A Collaborative Filtering Recommender System Model Using OWA and Uninorm Aggregation Operators

Ivan Palomares, Fiona Browne, Hui Wang, Peadar Davis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Recommender systems have played a prominent role in online platforms over the last decade. These systems have been incorporated into applications ranging from e-commerce to leisure, successfully enhancing user experience. Moreover, recommender systems are now being applied to a wider diversity of emerging context applications on the Internet including social media and online platforms for communities. In this study, we present a novel collaborative filtering recommender system model. This model differentiates from other recommender system models in that it utilizes two aggregation operators, namely OWA and uninorm, to compute similarity degrees between users. We demonstrate the application of the proposed model by integrating it in the HARMONISE platform for communities in the Urban Resilience domain. The application example illustrates how the proposed model of collaborative filtering recommender system can predict content of interest to users in the platform, based not only on user preferences but also on features of their user profile.
Original languageEnglish
Title of host publication2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE): Proceedings
Place of PublicationUnited States
Publisher IEEE
Pages382-388
Number of pages7
ISBN (Electronic)978-1-4673-9323-2
DOIs
Publication statusPublished - 14 Jan 2016

Publication series

NameInternational Conference on Intelligent Systems and Knowledge Engineering (ISKE): Proceedings

Bibliographical note

10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2015 ; Conference date: 24-11-2015

Keywords

  • fuzzy logic
  • recommender system
  • urban resilience

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