An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks
| Autor: | Teodoro Macias-Escobar, Laura Cruz-Reyes, César Medina-Trejo, Claudia Gómez-Santillán, Nelson Rangel-Valdez, Héctor Fraire-Huacuja |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Mathematical and Computational Applications, Vol 26, Iss 2, p 35 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2297-8747 1300-686X |
| DOI: | 10.3390/mca26020035 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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