About The Project

Our food recommender system is to offer personalized and healthy recipe recommendations


By using Matrix Factorization algorithm to learn user’s preference, the system will provide recipe recommendations that suit user’s personal taste.

Health and Well Being

The recommended recipe not only takes user’s personal taste into account but also considers the user’s nutritional balance and personal health.

Wearable Devices

The system has integrated the wearable devices to track the user’s daily activities to generate real-time and calorie-balanced recommendation.

Group Recommendation

The system will help a group of users like a family to coordinate what to cook and deal with the tastes from different group members.


They speak about us


Learn more about this feature packed App

Utility Function

Novel utility function integrating several criteria

User Preference Elicitation

User preference elicitation via modern technologies


Conversational recommendations.


Group recommendation preference aggregation.

Lifelog Tracking

Connecting lifelog tracking devices to food recommendations.

Human Computer Interaction

HCI for food recommender system.


Smart explanation tailored to support individual choices.

Profile Adaptation

Continuous user profile building and learning.


Browse Our Recent Publication List

“Integrating Wearable Devices into Mobile Food Recommender System”

Mouzhi Ge, David Massimo, Francesco Ricci, and Floriano Zini - 7th International Conference on Mobile Computing, Applications and Services 2015

“Health-aware Food Recommender System”

Mouzhi Ge, Francesco Ricci and David Massimo - ACM RecSys 2015

“Interaction Design in a Mobile Food Recommender System”

Mehdi Elahi, Mouzhi Ge, Francesco Ricci, Ignacio Fernández-Tobías, Shlomo Berkovsky, David Massimo - IntRS@ACM RecSys (2015)

“Using Tags and Latent Factors in a Food Recommender System”

Mouzhi Ge, Mehdi Elahi, Ignacio Fernández-Tobías, Francesco Ricci, David Massimo - ACM Digital Health 2015

“Multi-Criteria Food Recommender Systems”

Mouzhi Ge, Francesco Ricci, Floriano Zini - AIIA 2014

“Interactive Food Recommendation for Groups”

Mehdi Elahi, Mouzhi Ge, Francesco Ricci, David Massimo, Shlomo Berkovsky - ACM RecSys 2014


Take a closer look in more detail


Our team at the Free University of Bolzano, Italy

Prof. Francesco Ricci

Research Lead

Dr. Mouzhi Ge

Principal Investigator

Dott. David Massimo

Development Lead