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Recommender system based on pairwise association rules

Lookup NU author(s): Timur Osadchiy, Ivan Poliakov, Professor Patrick Olivier, Maisie Rowland, Dr Emma Foster

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 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.


Publication metadata

Author(s): Osadchiy T, Poliakov I, Olivier P, Rowland M, Foster E

Publication type: Article

Publication status: Published

Journal: Expert Systems with Applications

Year: 2019

Volume: 115

Pages: 535-542

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

URL: https://doi.org/10.1016/j.eswa.2018.07.077

DOI: 10.1016/j.eswa.2018.07.077


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