Recommender system based on pairwise association rules

Timur Osadchiy, Ivan Poliakov, Patrick Olivier, Maisie Rowland, Emma Foster

Research output: Contribution to journalArticleResearchpeer-review

39 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)535-542
Number of pages8
JournalExpert Systems with Applications
Volume115
DOIs
Publication statusPublished - Jan 2019
Externally publishedYes

Keywords

  • Association rules
  • Cold-start problem
  • Data mining
  • Ontologies
  • Recommender systems

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