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Managing Group Decision Making criteria values using Fuzzy Ontologies
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Consensus-Based Decision Support Model and Fusion Architecture for Dynamic Decision Making
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In: Electrical and Computer Engineering Publications (2022)
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Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics
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Consistency improvement with a feedback recommendation in personalized linguistic group decision making
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Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making
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The risk assessment of construction project investment based on prospect theory with linguistic preference orderings
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Revisiting Fuzzy and Linguistic Decision-Making: Scenarios and Challenges for Wiser Decisions in a Better Way
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Personalized individual semantics-based approach for large scale failure mode and effect analysis with incomplete preference information
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Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts
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Using Multi-granular Fuzzy Linguistic Modelling Methods to Represent Social Networks Related Information in an Organized Way
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A comparison between Fuzzy Linguistic RFM Model and traditional RFM model applied to Campaign Management. Case study of retail business.
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Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology
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Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets
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In: ISSN: 0884-8173 ; EISSN: 1098-111X ; International Journal of Intelligent Systems ; https://www.hal.inserm.fr/inserm-03026626 ; International Journal of Intelligent Systems, Wiley, 2019, 34 (6), pp.1261-1280. ⟨10.1002/int.22095⟩ (2019)
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Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets
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A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds
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Abstract:
One of the most difficult tasks for hedge funds investors is selecting a proper fund with just the right level level of risk. Often times, the issue is not only quantifying the hedge fund risk, but also the level the investors consider just right. To support this decision, we propose a novel recommender system, which is aware of the risks associated to different hedge funds, considering multiple factors, such as current yields, historic performance, diversification by industry, etc. Our system captures the preferences of the investors (e.g. industries, desired level of risk) applying fuzzy linguistic modeling and provides personalized recommendations for matching hedge funds. To demonstrate how our approach works, we have first profiled more than 4000 top hedge funds based on their composition and performance and second, created different simulated investment profiles and tested our recommendations with them. ; This paper has been developed with the FEDER financing under Project TIN2016-75850-R.
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URL: https://doi.org/10.1016/j.procs.2019.12.068 http://hdl.handle.net/10481/60242
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Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and Its Application on the Cloud Enterprise
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A Fuzzy Linguistic RFM Model Applied to Campaign Management
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Using clustering methods to deal with high number of alternatives on Group Decision Making
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An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions
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