Population-Level Analysis of Personalized Food Recommendation Using Reinforcement Learning.

This paper introduces an innovative methodology for optimizing recommendation strategies across different populations within the food industry. While previous approaches to recommending courses have overlooked cultural and age-based preferences, our work demonstrates how understanding these differences can significantly enhance the attractiveness for consumers and create new opportunities for marketing. By simulating diverse populations using a fuzzy logic approach, based on individual characteristics such as age, gender, geographical area, and city size, the study evaluates how recommendation algorithms perform within a generated menu database. Results show that algorithms like State-Action-Reward-State-Action (SARSA), multi-armed bandit (MAB), and Deep-Q Network (DQN) exhibit varying levels of efficiency depending on the population. Notably, the DQN improves accumulated reward over a random recommender by 71.60% for "Foodies", 65.02% for "Veggies", 63.46% for "Spanish", and 8.89% for "Seniors", while MAB achieves similar performance with fewer resources. Statistically significant differences (