Lookup NU author(s): Timur Osadchiy,
Professor Patrick Olivier,
Dr Emma Foster
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2018 The Authors Recommender systems based on methods such as collaborative and content-based filtering rely on extensive user profiles and item descriptors as well as on an extensive history of user preferences. Such methods face a number of challenges; including the cold-start problem in systems characterized by irregular usage, privacy concerns, and contexts where the range of indicators representing user interests is limited. We describe a recommender algorithm that builds a model of collective preferences independently of personal user interests and does not require a complex system of ratings. The performance of the algorithm is analyzed on a large transactional data set generated by a real-world dietary intake recall system.
Author(s): Osadchiy T, Poliakov I, Olivier P, Rowland M, Foster E
Publication type: Article
Publication status: Published
Journal: Expert Systems with Applications
Print publication date: 01/01/2019
Online publication date: 21/08/2018
Acceptance date: 10/07/2018
ISSN (print): 0957-4174
ISSN (electronic): 1873-6793
Publisher: Elsevier Ltd
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