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Article

An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks

by
Teodoro Macias-Escobar
1,2,*,†,
Laura Cruz-Reyes
3,†,
César Medina-Trejo
3,†,
Claudia Gómez-Santillán
3,†,
Nelson Rangel-Valdez
4,† and
Héctor Fraire-Huacuja
3,†
1
Department of Computing Science, Tijuana Institute of Technology, Av Castillo de Chapultepec 562, Tomas Aquino, Tijuana 22414, Mexico
2
Departamento de Ingeniería Informática, Universidad de Cádiz, 11519 Puerto Real, Spain
3
Graduate Program Division, Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, Cd. Madero 89440, Mexico
4
CONACyT Research Fellow at Graduate Program Division, Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, Cd. Madero 89440, Mexico
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Math. Comput. Appl. 2021, 26(2), 35; https://doi.org/10.3390/mca26020035
Submission received: 28 February 2021 / Revised: 20 April 2021 / Accepted: 20 April 2021 / Published: 21 April 2021
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2020)
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Abstract

The decision-making process can be complex and underestimated, where mismanagement could lead to poor results and excessive spending. This situation appears in highly complex multi-criteria problems such as the project portfolio selection (PPS) problem. Therefore, a recommender system becomes crucial to guide the solution search process. To our knowledge, most recommender systems that use argumentation theory are not proposed for multi-criteria optimization problems. Besides, most of the current recommender systems focused on PPS problems do not attempt to justify their recommendations. This work studies the characterization of cognitive tasks involved in the decision-aiding process to propose a framework for the Decision Aid Interactive Recommender System (DAIRS). The proposed system focuses on a user-system interaction that guides the search towards the best solution considering a decision-maker’s preferences. The developed framework uses argumentation theory supported by argumentation schemes, dialogue games, proof standards, and two state transition diagrams (STD) to generate and explain its recommendations to the user. This work presents a prototype of DAIRS to evaluate the user experience on multiple real-life case simulations through a usability measurement. The prototype and both STDs received a satisfying score and mostly overall acceptance by the test users.
Keywords: decision making process; cognitive tasks; recommender system; project portfolio selection problem; usability evaluation decision making process; cognitive tasks; recommender system; project portfolio selection problem; usability evaluation

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MDPI and ACS Style

Macias-Escobar, T.; Cruz-Reyes, L.; Medina-Trejo, C.; Gómez-Santillán, C.; Rangel-Valdez, N.; Fraire-Huacuja, H. An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks. Math. Comput. Appl. 2021, 26, 35. https://doi.org/10.3390/mca26020035

AMA Style

Macias-Escobar T, Cruz-Reyes L, Medina-Trejo C, Gómez-Santillán C, Rangel-Valdez N, Fraire-Huacuja H. An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks. Mathematical and Computational Applications. 2021; 26(2):35. https://doi.org/10.3390/mca26020035

Chicago/Turabian Style

Macias-Escobar, Teodoro, Laura Cruz-Reyes, César Medina-Trejo, Claudia Gómez-Santillán, Nelson Rangel-Valdez, and Héctor Fraire-Huacuja. 2021. "An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks" Mathematical and Computational Applications 26, no. 2: 35. https://doi.org/10.3390/mca26020035

APA Style

Macias-Escobar, T., Cruz-Reyes, L., Medina-Trejo, C., Gómez-Santillán, C., Rangel-Valdez, N., & Fraire-Huacuja, H. (2021). An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks. Mathematical and Computational Applications, 26(2), 35. https://doi.org/10.3390/mca26020035

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