Fuzzy Quantifier Induced By User Preference And Its Application Research

Fuzzy quantifiers play an important role in theoretical analysis(computer science,especially artificial intelligence)and practical applications.Personalized fuzzy quantifiers,have strong individual pertinence and can well reflect personal characteristics such as subjective preferences and decision-making attitudes of users.In recent years,they have gradually become the focus content of attention of many researchers.This thesis aims to improve the performance of personalized fuzzy quantifiers,take the expected value extraction ? personalized fuzzy quantifier modeling ?analysis idea of personalized product recommendation,a novel model for extracting expected value from users is presented,with which to establish a user preference-based personalized quantifier,and use it in personalized product recommendations.The specific research contents of this article are as follows:(1)Personalized Recommendation Model Based on Quantifier Induced by Preference.In order to obtain more effective personalized fuzzy quantifiers in order to improve the performance of personalized fuzzy quantifiers,apply the OWA(ordered weighted averaging)idea and TOPSIS(technique for order preference by similarity to ideal solution)method,a novel model for extracting expected value from users is presented,with which to establish a user preference-based personalized quantifier,and use it in personalized product recommendations.The developed model and quantifier can well capture and reflect many varieties of personality characteristics of users with different ability levels and knowledge structures.(2)Expansion research in prospect theory.The key data information for constructing personalized fuzzy quantifiers,that is,user expected value,is to be studied in depth.In fact,the essence of user expected value is the idea of reference point,As the core content of the prospect theory,the reference point plays a vital role in the performance of the related models in the prospect theory.Therefore,this thesis introduces the user expected value extraction model mentioned in(1)into theprospect theory,which effectively solves some problems in the traditional reference point acquisition method in the prospect theory,and provides a new reference direction for the source of reference point acquisition in the prospect theory.Case study and experimental results show that the developed model and quantifier can well capture and reflect many varieties of personality characteristics of users with different ability levels and knowledge structures.As such,the developed technique could be considered as an effective tool in practical applications for the“satisfactory solutions” in accord with some particular attitude,rather than the“optimal solutions” in general terms,characterized by greater applicability and flexibility by contrast with a similar kind of method.