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

ABSTRACT: 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 lev

SUBMITTER: Tellechea Y 

PROVIDER: S-EPMC12608990 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature