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dc.contributor.authorTrattner, Christoph
dc.contributor.authorElsweiler, David
dc.PublishedTrattner C, Elsweiler D. An evaluation of recommendation algorithms for online recipe portals. CEUR Workshop Proceedings. 2019;2439:24-28eng
dc.description.abstractBetter models of food preferences are required to realise the oft touted potential of food recommenders to aid with the obesity crisis. Many of the food recommender evaluations in the literature have been performed with small convenience samples, which limits our conidence in the generalisability of the results. In this work we test a range of collaborative iltering (CF) and content-based (CB) recommenders on a large dataset crawled from the web consisting of naturalistic user interaction data over a 15 year period. The results reveal strengths and limitations of diferent approaches. While CF approaches consistently outperform CB approaches when testing on the complete dataset, our experiments show that to improve on CF methods require a large number of users (> 637 when sampling randomly). Moreover the results show diferent facets of recipe content to ofer utility. In particular one of the strongest content related features was a measure of health derived from guidelines from the UK Food Safety Agency. This inding underlines the challenges we face as a community to develop recommender algorithms, which improve the healthfulness of the food people choose to eat.en_US
dc.publisherCEUR Workshop Proceedingseng
dc.rightsAttribution CC BYeng
dc.titleAn evaluation of recommendation algorithms for online recipe portalseng
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holderCopyright 2019 The Authorsen_US
dc.source.journalCEUR Workshop Proceedings

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