Fuzzy Target-Based Multi-feature Evaluation of Traditional Craft Products

Abstract

AbstractThis paper introduces our research work in fuzzy target-oriented decision analysis and its application to kansei-based evaluation of traditional crafts. After a brief introduction into fuzzy target-oriented decision analysis, we formulate a general target-oriented approach to multi-attribute evaluation problem for personalized recommendation. The central idea of this approach is to first interpret a particular user’s request as a target (or benchmark) at which the user would be only interested in candidates meeting this target, and then use a combination of target-oriented decision analysis and aggregation operators for defining an evaluation function that quantifies how well a candidate meets the user’s target. As for illustration, we will introduce a target-based evaluation method for multi-feature ranking of traditional craft products using kansei data and preferences specified by consumers, where product items are assessed according to the so-called kansei features, and kansei data are treated as categorical data.KeywordsOrder Weighted AveragePersonalized RecommendationOrder Weighted Average OperatorOpposite PairJapan Advance InstituteThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.