Population-level analysis of personalized food recommendation using reinforcement learning

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dc.contributor.authorTellechea Belzunce, Yone
dc.contributor.authorArrojo Magro, Markel
dc.contributor.authorCejudo Taramona, Ander
dc.contributor.authorMartín Andonegui, Cristina
dc.date.accessioned2025-12-10T09:30:29Z
dc.date.available2025-12-10T09:30:29Z
dc.date.issued2025-11-03
dc.date.updated2025-12-10T09:30:29Z
dc.description.abstractThis 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.en
dc.description.sponsorshipThis work is supported by the AISEJAN Research and Innovation project funded by the Government of the Basque Country under grant number ZL-2023/00296en
dc.identifier.citationTellechea, Y., Arrojo, M., Cejudo, A., & Martin, C. (2025). Population-level analysis of personalized food recommendation using reinforcement learning. Foods, 14(21). https://doi.org/10.3390/FOODS14213770
dc.identifier.doi10.3390/FOODS14213770
dc.identifier.eissn2304-8158
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4552
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2025 by the authors
dc.subject.otherConsumer preferences
dc.subject.otherEntire food supply chain
dc.subject.otherFood recommender systems
dc.subject.otherWaste reduction
dc.titlePopulation-level analysis of personalized food recommendation using reinforcement learningen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue21
oaire.citation.titleFoods
oaire.citation.volume14
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