Approaches for Extending Recommendation Models for Food Choices in Meals

Nguyen Nhung, Nguyen Nhung, Dao Nguyen, Dao Nguyen, Tiet Hong, Tiet Hong, Thi My Hang Vu, Thi My Hang Vu

2024

Abstract

In this paper, we propose food recommender systems based on users' historical food choices. Their advantage lies in providing personalized food suggestions for each user considering each meal. These systems are developed using two popular recommendation principles: neighbor-based and latent factor-based. In the neighbor-based model, the system aggregates the food choices of neighboring users to recommend food choices for the active user during the considered meal. In contrast, the latent factor-based model constructs and optimizes an objective function to learn positive representations of users, foods, and meals. In this new space, predicting users' food choices during meals becomes straightforward. Experimental results have demonstrated the effectiveness of the proposed models in specific cases. However, in a global statistical comparison, the latent factor-based model has proven to be more effective than the neighbor-based model.

Paper Citation

in Harvard Style

Nhung N., Nguyen D., Hong T. and My Hang Vu T. (2024). Approaches for Extending Recommendation Models for Food Choices in Meals. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 113-121. DOI: 10.5220/0013014000003838

in Bibtex Style

@conference{kdir24,
author={Nguyen Nhung and Dao Nguyen and Tiet Hong and Thi My Hang Vu},
title={Approaches for Extending Recommendation Models for Food Choices in Meals},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={113-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013014000003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Approaches for Extending Recommendation Models for Food Choices in Meals
SN - 978-989-758-716-0
AU - Nhung N.
AU - Nguyen D.
AU - Hong T.
AU - My Hang Vu T.
PY - 2024
SP - 113
EP - 121
DO - 10.5220/0013014000003838
PB - SciTePress