Name
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3297280.3297601 | SAC |
Keywords | Field | DocType |
contex-aware recommendations, recommendation explanation | Recommender system,Collaborative filtering,Information retrieval,Computer science,Product data,Extensibility,Cold start (automotive),Personalization | Conference |
ISBN | Citations | PageRank |
978-1-4503-5933-7 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mostafa Bayomi | 1 | 5 | 2.45 |
| Annalina Caputo | 2 | 95 | 21.48 |
| Matthew Nicholson | 3 | 1 | 2.12 |
| Anirban Chakraborty | 4 | 0 | 2.03 |
| Séamus Lawless | 5 | 111 | 30.18 |