Sánchez-Gómez, Claudia
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Preferred name
Sánchez-Gómez, Claudia
Official Name
Sánchez Gómez, Claudia Nallely
Alternative Name
cnsanchez
Main Affiliation
ORCID
Scopus Author ID
57201851558
Researcher ID
DTU-3417-2022

Research Output

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Landscape images distance using kullback leibler divergence

2018 , Sánchez-Gómez, Claudia , Mario Graff , Domínguez-Soberanes, Julieta , Gutiérrez, Sebastián

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Liking Product Landscape: Going Deeper into Understanding Consumers’ Hedonic Evaluations

2019 , Sánchez-Gómez, Claudia , Domínguez-Soberanes, Julieta , Héctor B. Escalona-Buendía , Mario Graff , Gutiérrez, Sebastián , Gabriela Sánchez

The use of graphical mapping for understanding the comparison of products based on consumers’ perceptions is beneficial and easy to interpret. Internal preference mapping (IPM) and landscape segmentation analysis (LSA) have successfully been used for this propose. However, including all the consumers’ evaluations in one map, with products’ overall liking and attributes’ perceptions, is complicated; because data is in a high dimensional space some information can be lost. To provide as much information as possible, we propose the liking product landscape (LPL) methodology where several maps are used for representing the consumers’ distribution and evaluations. LPL shows the consumers’ distribution, like LSA, and also it superimposes the consumers’ evaluations. However, instead of superimposing the average overall liking in one map, this methodology uses different maps for each consumer’s evaluation. Two experiments were performed where LPL was used for understanding the consumers’ perceptions and compared with classic methodologies, IPM and cluster analysis, in order to validate the results. LPL can be successfully used for identifying consumers’ segments, consumers’ preferences, recognizing perception of product attributes by consumers’ segments and identifying the attributes that need to be optimized.

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Analysis of wind missing data for wind farms in Isthmus of Tehuantepec

2018 , Sánchez-Gómez, Claudia , J. Enriquez-Zarate , Velázquez, Ramiro , Mario Graff , S. Sassi

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Recommendation System for a Delivery Food Application Based on Number of Orders

2023 , Sánchez-Gómez, Claudia , Domínguez-Soberanes, Julieta , Alejandra Arreola , Mario Graff

With the recent growth in food-delivery applications, creating new recommendation systems tailored to this platform is essential. State-of-the-art restaurant recommendation systems are based on users’ ratings or reviews, with data that are obtained from questionnaires or online platforms such as TripAdvisor, Zomato, Foursquare, or Yield. However, not all users give ratings or reviews after their purchase. This document proposes a recommendation system whose input is the number of orders stored by a real food-delivery application. These data are always available for all food-delivery applications and are stored all the time. Our proposal is based on the nearest-neighbor technique that calculates the client’s preferred restaurants and analyzes other clients with similar buying patterns. In addition, we propose a performance metric that can be used for this specific recommendation system that is based on real restaurant sales. We use a real dataset (available online) to validate our proposal. Based on our experiments, the recommendation system successfully gives only an average of 7.7 options from 187 that are available. We compared our proposal with other state-of-the-art recommendation techniques and obtained a better performance. Our results indicate that it is possible to generate recommendations based on the number of orders, making the use of a restaurant-recommendation system feasible in a real food-delivery application.

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Selecting crackling product based on sensory analysis by different statistical data approaches

2017 , G. Sanchez-Gutierrez , Domínguez-Soberanes, Julieta , G. Rodriguez-Serrano , H. Escalona-Buendia , Sánchez-Gómez, Claudia , Gutiérrez, Sebastián , Mario Graff

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I3GO+ at RICATIM 2017: A semi-supervised approach to determine the relevance between images and text-annotations

2017 , Jose Ortiz-Bejar , Eric S. Tellez , Mario Graff , Sabino Miranda-Jimenez , Daniela Moctezuma , Sánchez-Gómez, Claudia

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