Séminaires RALI-OLST

Following Good Examples - Health Goal-Oriented Food Recommendation based on Behavior Data

Yabo Ling


Le mercredi 20 avril 2022 à 11 h 30

Réunion Zoom, ci-dessous

Typical recommender systems try to mimic the past behaviors of users to make future recommendations. For example, in food recommendations, they tend to recommend the foods the user prefers. While the recommended foods may be easily accepted by the user, it cannot improve the user’s dietary habits for a specific goal such as weight control. In this presentation, we describe an approach to food recommendation trying to take into account the goal of the user (e.g. weight loss), the health information (BMI) of the user and the nutrition information of foods (calories). Instead of applying dietary guidelines as constraints, we build recommendation models from the successful behaviors of comparable users: the weight loss model is trained using the historical food consumption data of similar users who successfully lost weight. We tested the approach. Experiments on real data collected from a popular weight management app show that this recommendation approach can predict more appropriate foods than a traditional recommendation approach.

Recording: https://drive.google.com/file/d/1EHmI3hFgwP_6PzYG2KqERgxssRwHXY4H/view?usp=sharing

Joignez-vous à nous avec Zoom à l'aide de cette URL.
ID de réunion : 916 9097 5818, Code : 343273.
Numéros de téléphone : https://umontreal.zoom.us/u/abitNZzLg.
One tap mobile : +14388097799,,91690975818#,,,,,,0#,,343273#

Suivez ce lien pour vous inscrire à la liste de diffusion RALI-OLST.

Liste de tous les séminaires pour l'année :

1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024