本文選題：在線評(píng)論 + 網(wǎng)絡(luò)口碑��； 參考：《江蘇大學(xué)》2016年博士論文

【摘要】：隨著web2.0的快速發(fā)展,網(wǎng)絡(luò)數(shù)據(jù)幾何增長(zhǎng),快速、準(zhǔn)確獲取用戶需求信息成為相關(guān)企業(yè)和客戶的迫切需要,各種各樣的產(chǎn)品推薦系統(tǒng)應(yīng)運(yùn)而生。傳統(tǒng)推薦系統(tǒng)大多是以產(chǎn)品為中心,以用戶評(píng)分為數(shù)據(jù)源,系統(tǒng)性能依賴(lài)于用戶偏好模型的質(zhì)量,但用戶的偏好信息很難以簡(jiǎn)單的評(píng)分來(lái)全面表征。因此,推薦系統(tǒng)常出現(xiàn)冷啟動(dòng)、數(shù)據(jù)稀疏性等問(wèn)題。為了解決這些難題。本文提出以在線評(píng)論為基礎(chǔ)數(shù)據(jù)源,在對(duì)傳統(tǒng)網(wǎng)絡(luò)購(gòu)物推薦系統(tǒng)以及在線評(píng)論挖掘相關(guān)理論、技術(shù)、方法等進(jìn)行梳理、分析的基礎(chǔ)上:首先,作為網(wǎng)絡(luò)口碑的主要傳播形式,在線評(píng)論在已有文獻(xiàn)的研究中大都先驗(yàn)地被當(dāng)作外生變量來(lái)處理,并將兩者間的動(dòng)態(tài)相互關(guān)系簡(jiǎn)單的看成靜態(tài)單方向作用。本文在動(dòng)態(tài)內(nèi)生性假說(shuō)的視角下,引入在線評(píng)論各屬性變量外的可測(cè)量的控制變量與難以觀測(cè)或度量的啞變量,研究消費(fèi)者基于在線評(píng)論的產(chǎn)品網(wǎng)絡(luò)口碑感知問(wèn)題。在動(dòng)態(tài)面板數(shù)據(jù)模型中,通過(guò)控制變量與啞變量控制住內(nèi)生性的影響后發(fā)現(xiàn):(1)在靜態(tài)分析框架下,在線評(píng)論與網(wǎng)絡(luò)口碑感知之間是相互影響的,啞變量會(huì)對(duì)網(wǎng)絡(luò)口碑感知與在線評(píng)論之間同時(shí)產(chǎn)生影響;(2)在動(dòng)態(tài)分析框架下,在線評(píng)論與網(wǎng)絡(luò)口碑感知之間存在一定的跨期作用,但滯后期并不確定,并且負(fù)面在線評(píng)論的比例與網(wǎng)絡(luò)口碑感知之間并沒(méi)有本文預(yù)期的反饋效應(yīng),這說(shuō)明,二者的跨期動(dòng)態(tài)作用并不是相互的而是單方向的。通過(guò)分析網(wǎng)絡(luò)口碑感知的影響因素,確定在線評(píng)論的各屬性對(duì)消費(fèi)者網(wǎng)絡(luò)口碑感知的不同影響,識(shí)別關(guān)鍵因素,為在線評(píng)論信息的差異化挖掘提供依據(jù)。其次,在以上分析的基礎(chǔ)上,重點(diǎn)研究了在線評(píng)論的挖掘,包括在線評(píng)論數(shù)據(jù)源的挖掘以及在線評(píng)論信息的挖掘兩部分。不同于以往的在線評(píng)論分析數(shù)據(jù)直接取自網(wǎng)絡(luò)購(gòu)物平臺(tái)或?qū)I(yè)點(diǎn)評(píng)網(wǎng)站,本研究將整個(gè)互聯(lián)網(wǎng)作為在線評(píng)論的數(shù)據(jù)源,并從中挖掘可靠的數(shù)據(jù)源。通過(guò)將研究分解成三個(gè)子任務(wù),對(duì)網(wǎng)絡(luò)數(shù)據(jù)從運(yùn)用改進(jìn)的PageRank剔除作弊網(wǎng)頁(yè)開(kāi)始;再運(yùn)用改進(jìn)的TC-PageRank提煉與產(chǎn)品主題高度相關(guān)并包含大量在線評(píng)論數(shù)據(jù)的網(wǎng)頁(yè)集;到運(yùn)用改進(jìn)的HITS確定在線評(píng)論分析數(shù)據(jù)來(lái)源的權(quán)威網(wǎng)頁(yè)集結(jié)束。而對(duì)于在線評(píng)論信息的挖掘研究,在線評(píng)論作為潛在消費(fèi)者網(wǎng)購(gòu)的重要參考依據(jù),挖掘其有價(jià)值的信息是有效利用的關(guān)鍵。針對(duì)網(wǎng)購(gòu)平臺(tái)的設(shè)計(jì)原則以及消費(fèi)者的實(shí)際需求,融合社會(huì)化標(biāo)注構(gòu)建領(lǐng)域本體,基于領(lǐng)域本體的層次結(jié)構(gòu),將在線評(píng)論的特征詞映射為本體概念,并利用Jess推理引擎提取評(píng)論中的隱性產(chǎn)品屬性,再將概念間的層次關(guān)系映射到產(chǎn)品屬性中,構(gòu)建層次化產(chǎn)品屬性集�；趯盈BCRFs模型以及情感詞典,從在線評(píng)論的極性分析到句子級(jí)的情感強(qiáng)度分析再到產(chǎn)品屬性級(jí)的褒貶強(qiáng)度分析,實(shí)現(xiàn)在線評(píng)論情感傾向性的層次化分析。最后,針對(duì)網(wǎng)絡(luò)購(gòu)物推薦系統(tǒng)的數(shù)據(jù)稀疏性、冷啟動(dòng)問(wèn)題日益突出以及傳統(tǒng)的基于評(píng)分信息的用戶偏好信息采集的不足,導(dǎo)致推薦算法的推薦效果不能令用戶滿意的問(wèn)題,本文在上述網(wǎng)絡(luò)口碑感知影響因素分析的基礎(chǔ)上,提出了基于在線評(píng)論信息挖掘的用戶偏好模型以及產(chǎn)品特征模型的構(gòu)建方法�；趯哟位挠脩羝眯畔�,構(gòu)建了基于本體建模方法的動(dòng)態(tài)用戶偏好模型,并通過(guò)用戶偏好的更新本體進(jìn)行用戶偏好的增加、刪減以及調(diào)整,時(shí)刻保持用戶偏好本體的動(dòng)態(tài)更新。在構(gòu)建網(wǎng)絡(luò)購(gòu)物混合推薦模型之前,研究如何設(shè)計(jì)推薦系統(tǒng)才能獲得用戶的信任,進(jìn)而實(shí)現(xiàn)推薦系統(tǒng)的預(yù)期作用。利用管理學(xué)、心理學(xué)、信息學(xué)等相關(guān)理論和方法,基于人際信任理論將用戶對(duì)推薦系統(tǒng)的信任過(guò)程劃分為從初始信任到交互信任再到推薦信任的三個(gè)階段。探討了影響各階段信任的關(guān)鍵影響因素,并構(gòu)建了多階段用戶信任的綜合模型�；谠撗芯糠治隽擞脩粜湃渭安杉{推薦系統(tǒng)的關(guān)鍵影響因素,得出用戶感知可信并采納的推薦系統(tǒng)特征。在實(shí)證研究的基礎(chǔ)上,根據(jù)Walls等提出的ISDT框架,分別從元需求與元設(shè)計(jì)兩個(gè)方面對(duì)用戶感知可信并采納的推薦系統(tǒng)特征進(jìn)行了詳細(xì)闡述。根據(jù)上述研究結(jié)果,構(gòu)建了基于在線評(píng)論挖掘的網(wǎng)絡(luò)購(gòu)物混合推薦模型,將推薦細(xì)化到產(chǎn)品特征層次并按消費(fèi)者對(duì)產(chǎn)品的各特征評(píng)價(jià)進(jìn)行綜合排序。該模型以協(xié)同過(guò)濾算法為框架,結(jié)合基于內(nèi)容推薦算法,通過(guò)產(chǎn)品的多屬性評(píng)分來(lái)緩解稀疏性問(wèn)題,并通過(guò)基于用戶屬性的相似度與基于產(chǎn)品屬性的相似度計(jì)算算法在一定程度上解決了用戶冷啟動(dòng)與產(chǎn)品冷啟動(dòng)問(wèn)題;結(jié)合多種相似度算法構(gòu)建了基于用戶偏好與產(chǎn)品特征的混合推薦算法。仿真實(shí)驗(yàn)通過(guò)采集淘寶網(wǎng)、亞馬遜中國(guó)網(wǎng)、京東網(wǎng)這三個(gè)國(guó)內(nèi)大型網(wǎng)絡(luò)購(gòu)物平臺(tái)的手機(jī)頻道的10000條在線評(píng)論信息,驗(yàn)證了基于在線評(píng)論挖掘的網(wǎng)絡(luò)購(gòu)物混合推薦模型良好的推薦精確度以及解決冷啟動(dòng)問(wèn)題的能力。并基于上述的研究結(jié)果,探討了網(wǎng)絡(luò)購(gòu)物推薦系統(tǒng)的推薦策略以及網(wǎng)購(gòu)平臺(tái)在產(chǎn)品營(yíng)銷(xiāo)的管理實(shí)踐中的主要對(duì)策建議。結(jié)合全文研究,總結(jié)歸納本文主要研究?jī)?nèi)容與貢獻(xiàn),并闡述本文不足之處以及對(duì)后續(xù)研究的展望。
[Abstract]:With the rapid development of Web2.0, the geometric growth, rapid and accurate acquisition of user demand information has become an urgent need for the related enterprises and customers. All kinds of product recommendation systems emerge as the times require. Most of the traditional recommendation systems are product centered, users are rated as data sources, and the performance of the system depends on the quality of user preference models. However, the user's preference information is difficult to be characterized in a simple way. Therefore, the recommendation system often appears cold start, data sparsity and other problems. In order to solve these problems, this paper proposes the online comment based data source, the traditional network shopping recommendation system and the line comment mining related theories, techniques, methods and so on. On the basis of analysis, first, as the main form of communication of internet word-of-mouth, online reviews are mostly treated as exogenous variables in the study of existing literature, and the dynamic relationship between them is simply regarded as static single direction. This paper introduces online reviews from the perspective of dynamic endogenous hypothesis and introduces online reviews to various genera. The measurable control variables outside the sex variables and the dumb variables that are difficult to observe or measure, study the consumer product network word-of-mouth perception based on online reviews. In the dynamic panel data model, the effects of control variables and dumb variables are found to control the endogenous effects: (1) online comment and the sense of internet word-of-mouth under the static analysis framework There is a mutual influence between knowledge and the interaction between the dumb variables and the online comments. (2) under the framework of dynamic analysis, there is a certain intertemporal effect between the online comment and the internet word-of-mouth perception, but the delay is not definite, and the proportion of the negative online comments and the internet word-of-mouth perception is not predefined. The feedback effect of the period shows that the dynamic role of the two parties is not mutual but single direction. By analyzing the influence factors of the online word-of-mouth perception, we can determine the different effects of the attributes of online comments on the perception of WOM, identify the key factors and provide the basis for the differential mining of online comment information. On the basis of the analysis, it focuses on the mining of online reviews, including the two parts of the online comment data source and the online review information. Different from the previous online comment analysis data, the online reviews are directly derived from the online shopping platform or the professional review site. Excavate reliable data sources. By decomposing the research into three subtasks, network data begins with an improved PageRank culling web page; the improved TC-PageRank is used to extract a web set which is highly related to the product theme and contains a large number of online commentary data; to use the improved HITS to determine the online review analysis data source In the study of online review information, online review is an important reference for potential consumer online shopping. Mining its valuable information is the key to effective use. In view of the design principles of online shopping platform and the actual needs of consumers, social annotation is used to construct domain ontology based on the domain. The hierarchical structure of ontology maps the feature words of online reviews into ontology concepts, and uses Jess reasoning engines to extract the hidden product attributes in comments, and then maps the hierarchical relationship between concepts to product attributes and constructs hierarchical product attributes. Based on cascading CRFs models and emotional dictionaries, the polarity analysis of online reviews to sentences is made. The level of emotional intensity analysis and the analysis of the appreciation intensity of the product attribute level to realize the hierarchical analysis of the emotional tendencies of online reviews. Finally, the recommendation algorithm is recommended in view of the data sparsity of the online shopping recommendation system, the increasingly prominent cold start problem and the insufficient information collection of the traditional users based on the score information. In this paper, the user preference model based on online comment information mining and the construction method of product feature model are put forward on the basis of the analysis of the factors affecting the network word of mouth perception. Based on the hierarchical user preference information, a dynamic user preference model based on the ontology modeling method is constructed. And the user preferences are added, deleted and adjusted to maintain the dynamic update of the user's preference ontology. Before constructing the mixed recommendation model of the network shopping, it is studied how to design the recommendation system to obtain the trust of the user, and then realize the expected function of the recommendation system. Based on the theory and methods of Informatics, based on the interpersonal trust theory, the trust process of the user to the recommendation system is divided into three stages from the initial trust to the interactive trust to the recommended trust. The key influencing factors that affect the trust in various stages are discussed, and a comprehensive model of multistage user trust is constructed. The users trust and adopt the key influencing factors of the recommendation system, and obtain the characteristics of the recommendation system that the user perceiving and adopting. On the basis of the empirical research, according to the ISDT framework proposed by Walls and so on, the characteristics of the recommendation system of the users' perceived trust and adoption are described in detail from the two aspects of the meta demand and the meta design. As a result, a mixed recommendation model of online shopping based on online review mining is constructed, and the recommendation is refined to the product feature level and comprehensive ranking according to the consumer's evaluation of the product features. The model is based on the collaborative filtering algorithm and combines the content recommendation algorithm to alleviate the sparsity problem through the multi attribute score of the product. With the similarity degree based on user attributes and similarity calculation based on product attributes, the problem of cold start and cold start of product is solved to a certain extent, and a hybrid recommendation algorithm based on user preferences and product characteristics is constructed in combination with a variety of similarity algorithms. The simulation experiment is carried out by collecting Taobao network, Amazon China network, and Beijing. The 10000 online comment information on the mobile channel of the three large network shopping platforms of the East Network validates the good recommendation accuracy and the ability to solve the cold start problem based on the online shopping mixed recommendation model based on online review mining. Based on the above research results, the recommendation strategy of the network shopping recommendation system is discussed. And the main countermeasures and suggestions of the online shopping platform in the management practice of product marketing. Combined with the full text research, the main research content and contribution of this article are summed up, and the shortcomings of this paper and the prospect of the follow-up research are expounded.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：F724.6
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本文編號(hào)：1971342