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

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 crucia...
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Gespeichert in:

Autor*in:	Teodoro Macias-Escobar [verfasserIn] Laura Cruz-Reyes [verfasserIn] César Medina-Trejo [verfasserIn] Claudia Gómez-Santillán [verfasserIn] Nelson Rangel-Valdez [verfasserIn] Héctor Fraire-Huacuja [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	decision making process cognitive tasks recommender system project portfolio selection problem usability evaluation

Übergeordnetes Werk:	In: Mathematical and Computational Applications - MDPI AG, 2017, 26(2021), 2, p 35
Übergeordnetes Werk:	volume:26 ; year:2021 ; number:2, p 35

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.3390/mca26020035

Katalog-ID:	DOAJ014258056

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callnumber-first	T - Technology
author	Teodoro Macias-Escobar
spellingShingle	Teodoro Macias-Escobar misc T57-57.97 misc QA1-939 misc QA75.5-76.95 misc decision making process misc cognitive tasks misc recommender system misc project portfolio selection problem misc usability evaluation misc Applied mathematics. Quantitative methods misc Mathematics misc Electronic computers. Computer science An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks
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title_auth	An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks
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An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks

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