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.
Cite this article
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 -