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http://hdl.handle.net/10071/17057
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Title: Context-aware food recommendation system
Authors: Maia, R.
Ferreira, J. C.
Keywords: Context-aware
Food
Recommender system
Collaboration
Issue Date: 2018
Publisher: International Association of Engineers
Abstract: Recommendation systems are commonly used in websites with large datasets, frequently used in e-commerce or multimedia streaming services. These systems effectively help users in the task of finding items of their interest, while also being helpful from the perspective of the service or product provider. However, successful applications to other domains are less common, and the number of personalized food recommendation systems is surprisingly small although this particular domain could benefit significantly from recommendation knowledge. This work proposes a contextaware food recommendation system for well-being care applications, using mobile devices, beacons, medical records and a recommender engine. Users passing near a food place receives food recommendation based on available offers order by appropriate foods for everyone’s health at the table in real time. We also use a new robust recipe recommendation method based on matrix factorization and feature engineering, both supported by contextual information and statistical aggregation of information from users and items. The results got from the application of this method to three heterogeneous datasets of recipe’s user ratings, showed that gains are achieved regarding recommendation performance independently of the dataset size, the items textual properties or even the rating values distribution.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/17057
ISBN: 9789881404817
ISSN: 2078-0958
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-52686
Appears in Collections:ISTAR-CRI - Comunicações a conferências internacionais

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WCECS2018_pp349-356.pdfVersão Editora1.86 MBAdobe PDFView/Open


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