@article{Chen:2018:10.1109/TBCAS.2017.2760504,
author = {Chen, C-H and Karvela, M and Sohbati, M and Shinawatra, T and Toumazou, C},
doi = {10.1109/TBCAS.2017.2760504},
journal = {IEEE Transactions on Biomedical Circuits and Systems},
pages = {151--160},
title = {PERSON - Personalized Expert Recommendation System for Optimized Nutrition},
url = {http://dx.doi.org/10.1109/TBCAS.2017.2760504},
volume = {12},
year = {2018}
}

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TY  - JOUR
AB  - The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.
AU  - Chen,C-H
AU  - Karvela,M
AU  - Sohbati,M
AU  - Shinawatra,T
AU  - Toumazou,C
DO  - 10.1109/TBCAS.2017.2760504
EP  - 160
PY  - 2018///
SN  - 1932-4545
SP  - 151
TI  - PERSON - Personalized Expert Recommendation System for Optimized Nutrition
T2  - IEEE Transactions on Biomedical Circuits and Systems
UR  - http://dx.doi.org/10.1109/TBCAS.2017.2760504
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000423561900014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8089390
VL  - 12
ER  - 

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