Population-Level Analysis of Personalized Food Recommendation Using Reinforcement Learning

TL;DR

This paper shows how reinforcement learning can improve food recommendations by considering cultural and age-based preferences across different populations.

Contribution

The study introduces a population-level analysis using reinforcement learning to optimize food recommendations based on demographic factors.

Findings

DQN improves accumulated reward over random recommenders by 71.60% for 'Foodies' and 8.89% for 'Seniors'.

MAB achieves similar performance to DQN with fewer computational resources.

Statistically significant differences in DQN performance are found across populations with large effect sizes.

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 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 (p < 0.005) are found in the performance of the DQN between populations, with large effect sizes according to Cliff’s delta. These findings highlight recommender systems as an opportunity to navigate market demand, optimize supply chains, and reduce food waste. A better understanding of public preferences enables more effective alignment of supply and demand across the entire food supply chain. As a conclusion, while the DQN effectively captures target group preferences, the optimum recommendation strategy should be chosen by balancing algorithmic performance, computational efficiency, and the specific requirements of the food sector.

Figures