本文選題：人類行為學(xué) + 標(biāo)度律��； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：電子商務(wù)中用戶的評(píng)論意見為潛在消費(fèi)者提供了重要的參考依據(jù)。虛假評(píng)論在一定程度上誤導(dǎo)了消費(fèi)者的消費(fèi)傾向,使得消費(fèi)者逐漸喪失對(duì)電子商務(wù)評(píng)價(jià)系統(tǒng)的信任。準(zhǔn)確高效的對(duì)系統(tǒng)中的虛假評(píng)論進(jìn)行檢測(cè)和清除成為一個(gè)必須解決的關(guān)鍵問題。本論文針對(duì)上述問題,在深入分析電子商務(wù)中人類行為特征及演化規(guī)律的基礎(chǔ)上,基于人類行為學(xué)理論提取用戶評(píng)論的行為特征,采用模式挖掘的方式對(duì)個(gè)體虛假評(píng)論用戶以及虛假評(píng)論群組進(jìn)行檢測(cè)。主要研究內(nèi)容如下:1.基于實(shí)際數(shù)據(jù),采用去趨勢(shì)波動(dòng)分析法首次對(duì)電子商務(wù)中人類行為活動(dòng)標(biāo)度律進(jìn)行實(shí)證研究。研發(fā)發(fā)現(xiàn)人類購買和瀏覽行為的演化過程不同于無關(guān)聯(lián)的泊松過程,具有較強(qiáng)的長程關(guān)聯(lián)特性和自組織臨界性。2.個(gè)體虛假評(píng)論用戶檢測(cè)。提出了基于用戶行為聚類和基于用戶經(jīng)驗(yàn)演化兩種檢測(cè)算法。其中,基于用戶行為聚類的檢測(cè)算法通過對(duì)用戶評(píng)分行為聚類來計(jì)算用戶的從眾強(qiáng)度,采用二階段檢測(cè)的方法對(duì)用戶的信譽(yù)大小做出評(píng)估�；谟脩艚�(jīng)驗(yàn)演化的檢測(cè)算法根據(jù)用戶行為隨時(shí)間的演化特性,利用穩(wěn)定性和無序性兩個(gè)行為特征來刻畫用戶經(jīng)驗(yàn)大小,對(duì)用戶可信度進(jìn)行判斷。通過實(shí)驗(yàn)驗(yàn)證,兩種檢測(cè)算法均具有較高的檢測(cè)準(zhǔn)確度,優(yōu)于與之對(duì)比的基準(zhǔn)檢測(cè)算法,尤其在用戶評(píng)分較為稀疏的現(xiàn)實(shí)評(píng)價(jià)系統(tǒng)中檢測(cè)效果十分出色。3.虛假評(píng)論用戶群組檢測(cè)�；诠衾孀畲蠡僭O(shè),采用關(guān)聯(lián)規(guī)則對(duì)系統(tǒng)中的虛假評(píng)論群組進(jìn)行檢測(cè)。算法不僅可以挖掘虛假評(píng)論群組規(guī)模大小,也可以有效的對(duì)指定規(guī)模的群組進(jìn)行檢測(cè)。4.實(shí)現(xiàn)上述三種檢測(cè)算法,從數(shù)據(jù)導(dǎo)入,虛假評(píng)論用戶及群組檢測(cè),結(jié)果查看等方面,實(shí)現(xiàn)檢測(cè)系統(tǒng),完成了對(duì)電子商務(wù)中虛假評(píng)論用戶的檢測(cè)任務(wù)。本課題的創(chuàng)新點(diǎn)在于不依賴具體的評(píng)論形式和內(nèi)容,從分析用戶評(píng)論行為出發(fā),提出基于評(píng)論行為聚類和經(jīng)驗(yàn)演化的兩種虛假評(píng)論用戶檢測(cè)算法,在保證檢測(cè)準(zhǔn)確度的同時(shí)提高了檢測(cè)效率,具有較強(qiáng)的實(shí)用性。
[Abstract]:The comments of users in e-commerce provide important reference for potential consumers. To some extent, false comments mislead consumers' propensity to consume and make consumers lose their trust in electronic commerce evaluation system. Accurate and efficient detection and removal of false comments in the system has become a key problem that must be solved. In this paper, based on the analysis of the characteristics and evolution of human behavior in electronic commerce, the behavioral characteristics of user comments are extracted based on the theory of human behavior. The users and groups of individual false comments are detected by pattern mining. The main research contents are as follows: 1. Based on the actual data, the de-trend volatility analysis method is used for the first time to make an empirical study on the scale law of human behavior in electronic commerce. It is found that the evolutionary process of human purchase and browsing behavior is different from the independent Poisson process and has strong long-range correlation and self-organized criticality. Individual false comment user detection. Two detection algorithms based on user behavior clustering and user experience evolution are proposed. Among them, the detection algorithm based on user behavior clustering calculates the user's herd strength by clustering the user's scoring behavior, and evaluates the user's reputation by using the two-stage detection method. The detection algorithm based on the evolution of user experience describes the size of user experience according to the evolution characteristics of user behavior over time and uses two behavioral characteristics of stability and disorder to judge the reliability of users. The experimental results show that the two detection algorithms have high detection accuracy and are superior to the benchmark detection algorithm, especially in the practical evaluation system with sparse user scores. False comment user group detection. Based on the hypothesis of maximization of attack benefits, association rules are used to detect false comment groups in the system. The algorithm can not only mine the size of the false comment group, but also detect the specified size group effectively. The above three detection algorithms are implemented from the aspects of data import, false comment user and group detection, result checking and so on. The detection system is implemented and the detection task of false comment user in electronic commerce is completed. The innovation of this paper is that it does not depend on the specific form and content of comments, and from the analysis of user comment behavior, two kinds of false comment user detection algorithms based on comment behavior clustering and empirical evolution are proposed. At the same time, it improves the detection efficiency and has strong practicability.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F713.36;TP391.1
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本文編號(hào)：2036526