A Cold-start Resistant and Extensible Recommender System
Citation:
Mostafa Bayomi, Annalina Caputo, Matthew Nicholson, Anirban Chakraborty, Seamus Lawless, A Cold-start Resistant and Extensible Recommender System, ACM Symposium On Applied Computing, Limassol, Cyprus, April 8th-12th, 2019, 1665 - 1669Download Item:
CoRE-CR.pdf (Accepted for publication (author's copy) - Peer Reviewed) 404.5Kb
Abstract:
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.
Sponsor
Grant Number
Science Foundation Ireland (SFI)
13/RC/2106
Author's Homepage:
http://people.tcd.ie/selawles
Author: Lawless, Seamus
Other Titles:
ACM Symposium On Applied ComputingType of material:
Conference PaperCollections:
Availability:
Full text availableLicences: