PERSON-Personalised Expert Recommendation System for Optimised Nutrition
File(s)
Author(s)
Chen, Chih-Han
Type
Thesis
Abstract
The Nobel-Prize-winning concept of nudge theory aims to optimise the behaviour of individuals through making small changes in daily life. In the food industry, research into nudge theory is moving towards the application of healthier decisions via personalisation.
Deoxyribonucleic acid (DNA) provides the best basis for research into personalisation, as it is known to be the key to understanding the development and functioning of all living organisms. DNA analysis services have emerged in the consumer market, offering people access to their DNA patterns and relevant information. However, due to the high complexity and large amount of information involved, existing knowledge in this field is rarely applied in daily life. In order to employ the knowledge gained from genetic innovations within a market context, research into a framework that can provide optimised decisions to consumers, retailers, and manufacturers is of interest. In this work, I develop a personalised expert recommendation system for optimised nutrition (PERSON). This can perform personalised grocery product filtering for consumers and generate recommendations based on correlations between nutrition and genetics. I also construct a simulation framework called grocery business unionism strategy in neural expert system simulation (GBUSiNESS), which simulates consumer agent models and manufacturer agent models utilising PERSON in order to generate better product productions and marketing strategies for suppliers. To confirm the advantages of my expert recommendation systems, a trial was conducted with customers in a supermarket.
The contribution of the invented framework PERSON is in generating recommendations that can form the basis for better product decisions for consumers, with simulation framework GBUSiNESS that can enable retailers and manufacturers to improve food manufacturing based on genetic knowledge. This project is an example of DNAnudge, and can demonstrate the application of nudge theory to recommendation systems for personalised food decisions. It is expected to benefit both customers and manufacturers.
Deoxyribonucleic acid (DNA) provides the best basis for research into personalisation, as it is known to be the key to understanding the development and functioning of all living organisms. DNA analysis services have emerged in the consumer market, offering people access to their DNA patterns and relevant information. However, due to the high complexity and large amount of information involved, existing knowledge in this field is rarely applied in daily life. In order to employ the knowledge gained from genetic innovations within a market context, research into a framework that can provide optimised decisions to consumers, retailers, and manufacturers is of interest. In this work, I develop a personalised expert recommendation system for optimised nutrition (PERSON). This can perform personalised grocery product filtering for consumers and generate recommendations based on correlations between nutrition and genetics. I also construct a simulation framework called grocery business unionism strategy in neural expert system simulation (GBUSiNESS), which simulates consumer agent models and manufacturer agent models utilising PERSON in order to generate better product productions and marketing strategies for suppliers. To confirm the advantages of my expert recommendation systems, a trial was conducted with customers in a supermarket.
The contribution of the invented framework PERSON is in generating recommendations that can form the basis for better product decisions for consumers, with simulation framework GBUSiNESS that can enable retailers and manufacturers to improve food manufacturing based on genetic knowledge. This project is an example of DNAnudge, and can demonstrate the application of nudge theory to recommendation systems for personalised food decisions. It is expected to benefit both customers and manufacturers.
Version
Open Access
Date Issued
2019-08
Date Awarded
2020-03
Copyright Statement
Creative Commons Attribution-Non Commercial 4.0 International Licence
License URL
Advisor
Toumazou, Christofer
Publisher Department
Electrical and Electronic Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)