本文選題：匯聚設(shè)備　切入點(diǎn)：視頻流　出處：《電子科技大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：在信息時(shí)代的今天，互聯(lián)網(wǎng)應(yīng)用已滲透到各行各業(yè)乃至在日常生活中。在這種趨勢(shì)下，，電子商務(wù)高速發(fā)展，而在網(wǎng)絡(luò)上投放廣告已經(jīng)成為電子商務(wù)重要的營(yíng)銷方式。然而目前，粗放的廣告投放并不能得到讓人滿意的效果，甚至造成負(fù)面效果，因此針對(duì)用戶的興趣所在實(shí)施精準(zhǔn)廣告的定向投放是網(wǎng)絡(luò)廣告大勢(shì)所趨。 另一方面，隨著網(wǎng)絡(luò)帶寬躍升，網(wǎng)絡(luò)傳輸速度大幅提高，傳輸質(zhì)量有了相當(dāng)?shù)谋Ｕ希曨l流技術(shù)得到迅速發(fā)展和廣泛運(yùn)用。作為傳統(tǒng)電信領(lǐng)域的龍頭，運(yùn)營(yíng)商為了改變傳統(tǒng)的單純依靠帶寬收費(fèi)的盈利模式，運(yùn)營(yíng)商已經(jīng)進(jìn)行了很多新業(yè)務(wù)的嘗試，其中用戶定向廣告投放已經(jīng)進(jìn)入應(yīng)用推廣階段。 用戶興趣挖掘是定向廣告的基礎(chǔ)和前提。針對(duì)文字Web的興趣挖掘技術(shù)已經(jīng)相當(dāng)成熟，而基于視頻流的相關(guān)技術(shù)卻還在探索之中。論文首先介紹了目前的視頻流信息挖掘技術(shù)中的文本和行為信息的獲取及表示技術(shù)。 然后，論文討論了在匯聚設(shè)備針對(duì)視頻流而挖掘用戶興趣需要而且可以獲取哪些信息，如何得到這些信息。并且結(jié)合現(xiàn)有的技術(shù)，設(shè)計(jì)出針對(duì)視頻流領(lǐng)域特有的用戶興趣模型表示方法標(biāo)準(zhǔn)分類樹SCT(StandardCategoryTree)。標(biāo)準(zhǔn)分類樹方法是改進(jìn)的層次性本體論方法。使用這種方法判斷用戶興趣，首先需要建立樹狀層次的視頻領(lǐng)域標(biāo)準(zhǔn)分類，每個(gè)分類關(guān)聯(lián)特征詞的訓(xùn)練集和該分類對(duì)應(yīng)的人群概率。特征詞訓(xùn)練集用于對(duì)用戶的觀看視頻進(jìn)行分類，人群概率用于對(duì)用戶所屬人群的概率判斷。根據(jù)用戶觀看所觀看視頻的類型判斷用戶的人群概率，然后借助人群概率計(jì)算出用戶感興趣的商品分類和興趣度。 接著，論文設(shè)計(jì)并且實(shí)現(xiàn)了實(shí)驗(yàn)系統(tǒng)來(lái)驗(yàn)證標(biāo)準(zhǔn)分類樹方法在用戶興趣判斷方面的可行性和準(zhǔn)確性。實(shí)驗(yàn)的主要工作是建立標(biāo)準(zhǔn)分類樹，每個(gè)分類建立特征詞訓(xùn)練集，輸入用戶觀看視頻的歷史信息，包括視頻名稱、視頻分類、視頻描述、演員、地區(qū)、視頻時(shí)長(zhǎng)、用戶觀看日期和用戶觀看時(shí)長(zhǎng)，輸出該用戶對(duì)各商品分類的興趣度并排名。 最后，論文分析和總結(jié)了將標(biāo)準(zhǔn)分類樹方法應(yīng)用于視頻流領(lǐng)域的用戶興趣挖掘的優(yōu)點(diǎn)和缺點(diǎn)。并且對(duì)相關(guān)技術(shù)的發(fā)展進(jìn)行了展望。
[Abstract]:In the information age, Internet application has penetrated into all kinds of industries and even in daily life. Under this trend, E-commerce has developed rapidly, and placing advertisements on the Internet has become an important marketing method of E-commerce. Extensive advertising can not get satisfactory results, or even cause negative effects, so it is the trend of network advertising to target the interests of users to carry out targeted advertising. On the other hand, with the rise of the network bandwidth, the transmission speed of the network has been greatly improved, the transmission quality has been quite guaranteed, and the video streaming technology has been rapidly developed and widely used. In order to change the traditional profit mode which relies solely on bandwidth charges, operators have made a lot of new business attempts, among which targeted advertising has entered the stage of application promotion. User interest mining is the basis and premise of targeted advertising. Interest mining technology for text Web has been quite mature. However, the related technology based on video stream is still being explored. Firstly, this paper introduces the text and behavior information acquisition and representation technology in the current video stream information mining technology. Then, the paper discusses what information can be obtained and what information can be obtained by mining user interest in convergent devices for video streams, and combines with existing technologies. This paper designs a representation method of user interest model for video stream domain. The standard classification tree SCT / Standard Category tree is an improved hierarchical ontology method, which is used to judge user interest. First of all, it is necessary to establish the standard classification of video domain at the tree level, the training set of each classification association feature word and the corresponding crowd probability of the classification. The training set of feature words is used to classify the user's watching video. The crowd probability is used to judge the population probability of the user, and the group probability of the user is judged according to the type of video the user is watching, and then the classification and interest degree of the goods of interest to the user are calculated by the crowd probability. Then, the experiment system is designed and implemented to verify the feasibility and accuracy of the standard classification tree method in judging the user's interest. The main work of the experiment is to establish the standard classification tree, and each classification establishes the training set of feature words. Input the historical information of the user to watch the video, including the video name, video classification, video description, actor, region, video duration, user viewing date and user viewing time, output the user's interest in each item classification and rank. Finally, the paper analyzes and summarizes the advantages and disadvantages of applying the standard classification tree method to user interest mining in video streaming field, and prospects the development of related technologies.
【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】：2012
【分類號(hào)】：TP393.09

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