本文關(guān)鍵詞：二型模糊決策方法及其在個(gè)性化推薦中的應(yīng)用　出處：《東南大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 二型模糊集 信息集成 多屬性決策 粒計(jì)算 個(gè)性化推薦

【摘要】：目前,基于大數(shù)據(jù)驅(qū)動(dòng)的管理與決策方法研究,是管理科學(xué)領(lǐng)域研究的熱點(diǎn)問題。由于決策環(huán)境和決策行為的復(fù)雜性,在大數(shù)據(jù)背景下如何根據(jù)用戶的行為偏好和知識(shí)發(fā)現(xiàn),實(shí)現(xiàn)對(duì)用戶的個(gè)性化推薦,以及如何將多屬性決策方法與個(gè)性化推薦系統(tǒng)進(jìn)行有機(jī)結(jié)合,研究基于多屬性決策理論的個(gè)性化推薦系統(tǒng)就具有重要的理論價(jià)值與現(xiàn)實(shí)意義。本論文將主要利用二型模糊集對(duì)語言和語義信息的強(qiáng)大處理能力,以二型模糊信息集成與決策方法為切入點(diǎn),結(jié)合粒計(jì)算的相關(guān)方法和技術(shù),研究基于二型模糊決策方法的個(gè)性化推薦模型,為大數(shù)據(jù)背景下的復(fù)雜、動(dòng)態(tài)、信息不完全個(gè)性化推薦問題研究提供新的思路和解決方案。論文主要研究內(nèi)容如下：(1)基于信息集成理論,提出了基于Maclaurin對(duì)稱平均的區(qū)間二型模糊信息集成算子,并給出了其對(duì)偶形式及指數(shù)擴(kuò)展形式。研究了其參數(shù)單調(diào)性,并指出相較于現(xiàn)有的區(qū)間二型模糊信息集成算子,區(qū)間二型模糊Maclaurin對(duì)稱平均算子能夠柔性地處理具有多重關(guān)聯(lián)關(guān)系的區(qū)間二型模糊信息的集成問題。在此基礎(chǔ)上,提出了一種處理區(qū)間二型模糊決策問題的方法,并將其應(yīng)用于中國科技論文在線的論文評(píng)審?fù)扑]系統(tǒng)中。為研究具有關(guān)聯(lián)關(guān)系的二型模糊決策問題提供新的方法。(2)基于多屬性決策理論,分別從排序方法、效用模型和優(yōu)化模型三個(gè)方面研究了基于區(qū)間二型模糊信息的多屬性決策方法。首先,針對(duì)二型模糊集中的—個(gè)難點(diǎn)問題：排序問題。給出了一種基于三種初等平均的組合排序值方法。并且從數(shù)學(xué)的角度證明了該排序方法不但滿足線性序(全序)關(guān)系,而且還滿足admissiable序關(guān)系。在此基礎(chǔ)上,進(jìn)一步研究了基于組合排序值的區(qū)間二型模糊決策方法。然后,基于行為決策理論,借鑒行為經(jīng)濟(jì)學(xué)中的柔性三參數(shù)(FTP)效用函數(shù),給出了基于FTP效用函數(shù)的二型模糊OWA算子,并針對(duì)大規(guī)模復(fù)雜決策問題,提出了基于模糊聚類的區(qū)間二型模糊多屬性決策方法。進(jìn)一步將前景理論與經(jīng)典的VIKOR方法進(jìn)行結(jié)合,研究了基于動(dòng)態(tài)參考點(diǎn)的區(qū)間二型模糊行為決策方法,并將其應(yīng)用于高新技術(shù)風(fēng)險(xiǎn)投資評(píng)估推薦系統(tǒng)中。最后,將多目標(biāo)優(yōu)化中的LINMAP(多維線性規(guī)劃)方法擴(kuò)展到了區(qū)間二型模糊環(huán)境下,研究了決策者偏好的提取方法,建立了一系列屬性權(quán)重信息不完全時(shí)的優(yōu)化決策模型,并將其運(yùn)用在了手機(jī)購買的個(gè)性化推薦中。這些新方法的提出,進(jìn)一步豐富了二型模糊決策方法的理論體系,同時(shí)也擴(kuò)展了二型模糊決策理論在處理個(gè)性化商務(wù)推薦的應(yīng)用范圍。(3)基于粒計(jì)算理論,研究了個(gè)性化推薦中的評(píng)分矩陣的稀疏性問題。以粒計(jì)算方法為切入點(diǎn),建立了以Coverage和Specificity準(zhǔn)則為核心的協(xié)同優(yōu)化模型。提出了求解該模型的智能優(yōu)化算法。在一定程度上克服了現(xiàn)有的基于矩陣分解和變分優(yōu)化方法所帶來的高計(jì)算復(fù)雜性,為解決個(gè)性化推薦中的瓶頸問題提供了新的研究手段和方法。(4)基于二型模糊決策方法研究了個(gè)性化推薦模型。以兩種新的多屬性決策方法BTW和MULTIMOORA為基礎(chǔ),結(jié)合最優(yōu)信息粒建模的思想,提出了基于多屬性協(xié)同過濾和內(nèi)容的混合推薦模型。對(duì)于推薦模型求解中的參數(shù)設(shè)置問題,研究了個(gè)性化、差異化的參數(shù)設(shè)置方法。本文中所提到的方法都在二型模糊多屬性決策問題中得到了應(yīng)用。相關(guān)研究成果在理論層面上可以進(jìn)一步豐富和完善基于二型模糊信息的決策理論與方法；在應(yīng)用層面上可以為電子商務(wù)的個(gè)性化推薦提供新的工具與方法。
[Abstract]:At present, the research of management and decision making method based on data driven, is a hot issue in the field of management science research. Because of the complexity of the decision environment and decision-making behavior, in the context of large data according to user preferences and knowledge discovery, realize personalized recommendation to users, and how to use the multi attribute decision making method and personalized recommendation system by combining the research of personalized recommendation system based on multi-attribute decision theory has important theoretical value and practical significance. This paper will mainly use the type two fuzzy sets the powerful processing ability of language and semantic information, to type two fuzzy information integration and decision method as the starting point, combined with the relevant methods of granular computing and technology study of type two fuzzy decision making method based on Personalized Recommendation Model for large data under the background of complex, dynamic, personalized recommendation problem with incomplete information The research provides new ideas and solutions. The main contents of this thesis are as follows: (1) based on the theory of information integration, puts forward the interval Maclaurin symmetric mean type two fuzzy operator based on information integration, and gives its dual form and index extended form were studied. The parameters of the single tone, and points out that compared with the existing range of two fuzzy information aggregation operator, interval type two fuzzy Maclaurin symmetric mean operator can flexibly deal with the multiple correlation interval type two fuzzy information integration problems. On this basis, put forward a method of processing interval type two fuzzy decision problems, and its application in China sciencepaper online paper review recommendation system. Provide a new method of type two fuzzy decision problem as the research has a relationship. (2) based on multi-attribute decision-making theory, respectively from the sorting method, the utility model and optimization Three aspects of research model of multi attribute decision making method based on interval type two fuzzy information. Firstly, according to the type two fuzzy sets is a difficult problem: scheduling problem. A value of three methods of combination of elementary average sorting based on given. And from the angle of mathematics proves that the method can not only satisfy the linear order sequence (total order), but also meet the admissiable relation. On this basis, further research on the fuzzy decision making method based on interval value ranking combination type two. Then, the behavioral decision theory based on reference number of flexible three parameters in Behavioral Economics (FTP) utility function, gives the fuzzy OWA operator based on FTP utility function type two, and for large and complex decision problem, we propose a fuzzy multi attribute decision making method based on fuzzy clustering interval two. Further VIKOR method combining with classic prospect theory, was researched. From the dynamic point of reference interval type two fuzzy behavior decision method, and its application in the high-tech investment risk assessment recommendation system. Finally, the LINMAP in multi-objective optimization (multidimensional linear programming) method is extended to the interval type two fuzzy environment, extraction method studied the preference of decision makers, established a a series of incomplete information on attribute weights and the optimal decision-making model and its application in personalized recommendation to buy mobile phone. The new method, further enrich the theoretical system of type two fuzzy decision making method, but also extends the type two fuzzy decision theory in the application scope of processing personalized business recommended (3). Based on Granular Computing Theory and sparsity problem of personalized recommendation in the score matrix. Based on granular computing method as the starting point, based on the Coverage and Specificity standards as the core of the collaborative optimization model. The intelligent optimization algorithm to solve the model. In a certain extent overcome the high computational complexity of matrix decomposition and variational optimization method based on the existing brought, provide a new research means and methods for solving the bottleneck problem in personalized recommendation. (4) type two fuzzy decision making method based on personalized recommendation model. Two kinds of multi attribute decision making method of new BTW and MULTIMOORA as the foundation, combined with the optimal particle information modeling method, put forward the recommendation model for hybrid multiple attribute collaborative filtering and content-based recommendation model. The parameters for the solution of the problem of setting, personalized, parameter setting method has been applied to differentiation. The method mentioned in this paper are type two fuzzy multiple attribute decision making problems. The relevant research results in theory can further enrich and improve the type two fuzzy information based on decision theory. On the application level, it can provide new tools and methods for the personalized recommendation of e-commerce.

【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3;F274
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本文編號(hào)：1378830