 Implementing Recommendation Algorithms for Decision Making ProcessesPaula-Ligia Stanciu () and Razvan Petrusel () Informatica Economica, 2012, vol. 16, issue 3, 87-104 Abstract: This paper’s contribution is placed into decision-making process research area. In our previous papers we showed how decision maker’s behavior can be captured in logs and how an aggregated decision data model (DDM) can be mined. We now introduce two recommendation algorithms that rely on a DDM. Each algorithm aims to steer the decision maker’s actions towards a fully informed decision by suggesting the next action to be performed. The first algorithm is a Greedy approach that recommends the most frequent activities performed by other decision makers. The second algorithm is inspired from A* path finding algorithm. It finds a decision sub-objective and tries to guide the decision maker on the path to it. We eval-uate these algorithms by comparing them with each other and to the classical association rules approach. It is not our intention to recommend the better decision alternative. We want to make sure the decision makers made an informed decision by correctly and completely evaluating all alternatives. This is an extended version of the paper published at IE 2012 Conference. Keywords: Decision Data Model; Recommendation Algorithm; Decision Process; Decision Path; Decision Maker Activity (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:16:y:2012:i:3:p:87-104 Access Statistics for this article Informatica Economica is currently edited by Ion Ivan More articles in Informatica Economica from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC. |
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