CoRE: A Cold-Start Resistant and Extensible Recommender System

Mostafa Bayomi, Annalina Caputo, Matthew Nicholson, Anirban Chakraborty, Sèamus Lawless

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

Abstract / Description of output

In this paper, we propose the Cold-start Resistant and Extensible Recommender (CoRE), a novel recommender system that was developed as part of collaborative research with Ryanair, the world's most visited airline website. CoRE is an algorithmic approach to the recommendation of hotel rooms that can function in extreme cold-start situations. It is a hybrid recommender that blends elements of naïve collaborative filtering, content-based recommendation and contextual suggestion to address the various shortcomings which exist in the underlying user and product data. We evaluated the performance of CoRE in a number of scenarios in order to assess different aspects of the algorithm: personalization, multi-model and the resistance to the extreme cold-start situations. Experimental results on an authentic, real-world dataset show that CoRE effectively overcomes the different problems associated with the underlying data in these scenarios.
Original languageEnglish
Title of host publicationProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery, Inc
Number of pages4
ISBN (Print)9781450359337
Publication statusPublished - 8 Apr 2019
EventThe 34th ACM/SIGAPP Symposium On Applied Computing 2019 - Limassol, Cyprus
Duration: 8 Apr 201912 Apr 2019
Conference number: 34


SymposiumThe 34th ACM/SIGAPP Symposium On Applied Computing 2019
Abbreviated titleSAC 2019
Internet address

Keywords / Materials (for Non-textual outputs)

  • contex-aware recommendations
  • recommendation explanation


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