Evolutionary Approach in Recommendation Systems for Complex Structured Objects

Ortiz-Viso, Bartolomé

doi:10.1145/3383313.3411455

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…The main task of the generator is to combine the previous data sources and produce a dietary recommendation that fits nutritional levels with the other characteristics (meal size, preferences, meal behavior, health issues). The engine selected for this section follows the structure described in study [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

et al. 2023

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Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user’s needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

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Section: Methodsmentioning

confidence: 99%

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

et al. 2023

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show abstract

Section: Generatormentioning

confidence: 99%

Stance4Health nutritional APP: A Path to Personalized Smart Nutrition

Hinojosa-Nogueira¹,

Ortiz-Viso²,

Navajas-Porras³

et al. 2022

Preprint

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Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

show abstract