A Personalized Restaurant Recommendation System Using ML-TOPSIS Approach

DOI

10.2991/978-94-6463-496-9_21How to use a DOI?

Keywords

Recommender System; Machine Learning; Food and Restaurant Recommendation; locations; Nutrition; multi-criteria methods; machine learning

Abstract

Recommendation systems represent complex algorithms that direct the user to interesting resources within the vast data space available on the Internet, taking into account his personal information, preferences, etc. Machine learning and multi-criteria methods have brought about significant development in recommendation systems, providing personalized and accurate solutions for recommending products or services, etc. However, the problem that machine learning models face is that they tend to lack robustness and accuracy if they lack features that help personalize recommendations. In this paper, we address this problem by proposing a new system known as ML-TOPSIS (Machine Learning and Preference Ranking Technique by Similarity to Ideal Solution) for personalized restaurant recommendations based on health, location, and ratings. The primary goal of this system is to develop an application that attempts to provide food from restaurants that matches a person’s health status using the multi-criteria TOPSIS method, as well as their geographic location and similar ratings using machine learning algorithms based on collaborative filtering. By considering the nutritional requirements of individuals, especially for individuals suffering from obesity and diabetes. The results of the proposed system show that it helps users find restaurants according to their needs and aspirations to provide better suggestions.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Maroua Chemlal
AU  - Amina Zedadra
AU  - Ouarda Zedadra
AU  - Med Nadjib Kouahla
PY  - 2024
DA  - 2024/08/31
TI  - A Personalized Restaurant Recommendation System Using ML-TOPSIS Approach
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 270
EP  - 285
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_21
DO  - 10.2991/978-94-6463-496-9_21
ID  - Chemlal2024
ER  -