Use of Artificial Intelligence in MCDM

Perny, Patrice; Pomerol, Jean‐Charles

doi:10.1007/978-1-4615-5025-9_15

Cited by 5 publications

(2 citation statements)

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“…For this reason, the method is not based on rules applied like in expert systems, but rules are only used to automatically generate profile limits between the categories of an intermediate node and its parent. For this purpose, rules have the advantage that are more informative than mathematical functions (Perny and Pomerol 1999) and suitable when dealing with qualitative scales.…”

Section: Robustness Analysismentioning

confidence: 99%

A hierarchical multi-criteria sorting approach for recommender systems

et al. 2015

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Classification problems refer to the assignment of alternatives to predefined categories. In this work we focus on ordered classification, called sorting, in which the predefined categories indicate several degrees of interest or suitability of alternatives for a certain user. The assignment of alternatives is based on multiple conflicting criteria. This multi-criteria sorting approach is specially interesting for recommender systems aimed at finding the most suitable alternatives for each user. First, we study the ELECTRE-TRI-B sorting method, which follows the outranking approach based on comparing the evaluations of alternatives with the profile limits separating the categories. The complexity of some recommenders systems requires the extension of the classical ELECTRE-TRI-B method to manage a taxonomical organization of the set of criteria. In this paper we consider a set of criteria in the form of a hierarchy. The intermediate criteria in such a hierarchy correspond to different aspects of the recommendation procedure, such as content, context or cost. At each of these criteria, a sorting problem must be solved. Therefore, we propose extending Luis Del Vasto-Terrientes J Intell Inf Syst ELECTRE-TRI-B to handle assignments of alternatives on several levels of the hierarchy. A hierarchical procedure for sorting is proposed, called ELECTRE-TRI-B-H. Secondly, the paper explains the integration of ELECTRE-TRI-B-H into a recommender system of touristic activities related to wine, called GoEno-Tur. This system is developed for the region of Tarragona, Catalonia (Spain), which is a well-recognized area of wine and cava production.

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Section: Robustness Analysismentioning

confidence: 99%

A hierarchical multi-criteria sorting approach for recommender systems

et al. 2015

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“…Unfortunately, although this preference relation is total, there can be cycles. We adapt an example from (Perny and Pomerol 1999) in our context:…”

Section: ñJmentioning

confidence: 99%

Markov Decision Processes with Ordinal Rewards: Reference Point-Based Preferences

Weng

2011

ICAPS

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In a standard Markov decision process (MDP), rewards are assumed to be precisely known and of quantitative nature. This can be a too strong hypothesis in some situations. When rewards can really be modeled numerically, specifying the reward function is often difficult as it is a cognitively-demanding and/or time-consuming task. Besides, rewards can sometimes be of qualitative nature as when they represent qualitative risk levels for instance. In those cases, it is problematic to use directly standard MDPs and we propose instead to resort to MDPs with ordinal rewards. Only a total order over rewards is assumed to be known. In this setting, we explain how an alternative way to define expressive and interpretable preferences using reference points can be exploited.

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Integrating new information and communication technologies in a group decision support system

Karacapilidis

2000

International Transactions in Operational Research

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Use of Artificial Intelligence in MCDM

Cited by 5 publications

References 95 publications

A hierarchical multi-criteria sorting approach for recommender systems

A hierarchical multi-criteria sorting approach for recommender systems

Markov Decision Processes with Ordinal Rewards: Reference Point-Based Preferences

Integrating new information and communication technologies in a group decision support system

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