Work

Towards a fully personalized food recommendation tool

Year: 2018

Type: conference-paper

Abstract: We present1 a personalized ingredient-based Deep Learning recommender on the food domain that exploits ingredients and nutrition information to create recipe representations and propose to every user a more personalized and healthier meal. The recommender will be a critical component in our Meal Prediction Tool (MPT) designed with a focus on the personalization of services, increasing business efficiency and sustainability in the hospitality, restaurant and catering (HoReCa) industry.

Source: International Conference on Advanced Visual Interfaces (AVI)

DOI: 10.1145/3206505.3206590

Authors: Angelos Nezis, Haris Papageorgiou, Pavlos Georgiadis, Petr Jiskra, Dimitris Pappas, Maria Pontiki

Institutions: Athens University of Economics and Business, Athena Research and Innovation Center In Information Communication & Knowledge Technologies

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: green

Funders: European Commission, Horizon 2020 Framework Programme