Multi-view fuzzy information fusion in collaborative filtering recommender systems: Application to the urban resilience domain

Ivan Palomares Carrascosa*, Fiona Browne, Peadar Davis

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

14 Citations (Scopus)
34 Downloads (Pure)


Recommender systems play an increasingly important role in on-line web services for the personalization and recommendation of content to individual users. The quantity and quality of user-based information has progressed presenting the opportunity to further tailor recommendations to users based on feature view integration. In this work, we propose a hybrid framework which combines a collaborative filtering recommendation system with fuzzy decision-making approaches (based on the use of aggregation functions) to improve the accuracy of domain-specific recommendations. We extend upon the classical, neighborhood-based collaborative filtering process by conflating preference information with user-profile data in the recommendation process. This is performed using intelligent information fusion techniques whereby Ordered Weighted Averaging (OWA) operators and uninorm aggregation functions are implemented in the fusion of multiple views of pairwise similarity degrees between users. To address the shortcoming of generating sensible recommendations to cold users, we incorporate a novel weighting scheme based on fuzzy set modeling within the uninorm-based aggregation of similarity views. We finally outline the application of the proposed approach through an empirical study based in the Urban Resilience domain, along with an example to movie recommendation.
Original languageEnglish
Pages (from-to)64-80
Number of pages17
JournalData and Knowledge Engineering
Early online date24 Oct 2017
Publication statusPublished - Jan 2018

Structured keywords

  • Jean Golding


  • Collaborative filtering recommender systems
  • urban resilience
  • uninorm
  • ordered weighted averaging
  • fuzzy aggregation
  • multi-view similarity information fusion

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