本文選題：微博 + 線性加權(quán)�。� 參考：《科學(xué)技術(shù)與工程》2017年22期

【摘要】：針對(duì)當(dāng)前微博影響力度量算法中多集中于用戶行為屬性,忽略博文、結(jié)點(diǎn)本身價(jià)值的問題,從微博用戶信息出發(fā),以線性加權(quán)模型為基礎(chǔ),綜合分析用戶的行為屬性、博文相似度、節(jié)點(diǎn)相似度,創(chuàng)建影響力評(píng)價(jià)指標(biāo)體系。利用Page Rank算法思想,提出了基于用戶行為和博文內(nèi)容的用戶影響度量模型(user influence measurement rank,UMR)。通過采用新浪微博真實(shí)數(shù)據(jù)集測(cè)試,計(jì)算用戶的影響力,驗(yàn)證了UMR算法在博文內(nèi)容的基礎(chǔ)上,能客觀地反映用戶的交互行為,消除僵尸用戶對(duì)排序的影響,因而更科學(xué)、更合理。
[Abstract]:Aiming at the problem that the current micro - blog influence metric algorithm focuses on user behavior attributes , ignoring the value of the blog and the node itself , starting from the microblog user information , based on the linear weighted model , comprehensively analyzing the user ' s behavior attributes , the blog - text similarity , the node similarity , and creating the influence evaluation index system , and the page Rank algorithm is utilized to propose user influence measurement rank ( UMR ) based on user behavior and blog content . By adopting the real data set test of Sina Weibo , the influence of the user is calculated , and the UMR algorithm can objectively reflect the interaction behavior of the user on the basis of the content of the blog and eliminate the influence of the botnet user on the ordering , thus being more scientific and more reasonable .
【作者單位】： 江西理工大學(xué)信息工程學(xué)院;
【基金】：江西省研究生創(chuàng)新專項(xiàng)基金(YC2016-S316)資助
【分類號(hào)】：TP393.092
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本文編號(hào)：1962290