【摘要】：隨著社交網(wǎng)絡(luò)的快速發(fā)展,出現(xiàn)了像Facebook、Twitter和YouTube這樣成功擁有海量用戶的社交網(wǎng)站。社交網(wǎng)絡(luò)作為一種共享知識(shí)、與朋友聯(lián)系互動(dòng)的媒介,在我們生活中起到越來越重要的作用。標(biāo)簽分類是社交網(wǎng)絡(luò)中的一項(xiàng)重要應(yīng)用,例如在社交網(wǎng)絡(luò)中的用戶具有興趣標(biāo)簽和好友關(guān)系標(biāo)簽。此外,用戶也可以給社交網(wǎng)絡(luò)中的各種文本、圖片、視頻信息打標(biāo)簽。在傳統(tǒng)標(biāo)簽分類中,網(wǎng)絡(luò)數(shù)據(jù)由單個(gè)標(biāo)簽表示。但隨著各種社交網(wǎng)絡(luò)應(yīng)用的豐富,網(wǎng)絡(luò)數(shù)據(jù)的形式也越來越多樣化,單個(gè)標(biāo)簽已無法滿足社交網(wǎng)絡(luò)數(shù)據(jù)復(fù)雜和多語(yǔ)義的特性。因此,社交網(wǎng)絡(luò)環(huán)境下的多標(biāo)簽分類研究得到了越來越多的關(guān)注�；诖�,本文將針對(duì)社交網(wǎng)絡(luò)結(jié)構(gòu)分析、社交網(wǎng)絡(luò)環(huán)境下的多標(biāo)簽分類以及多標(biāo)簽在推薦系統(tǒng)中的應(yīng)用三個(gè)方面進(jìn)行研究。本文的主要工作如下：(1)介紹了社交網(wǎng)絡(luò)環(huán)境下多標(biāo)簽分類的產(chǎn)生背景和研究意義,分析了社交網(wǎng)絡(luò)結(jié)構(gòu)分析、多標(biāo)簽分類以及推薦系統(tǒng)的研究現(xiàn)狀和研究缺陷,并詳述了相關(guān)領(lǐng)域的概念、分類、關(guān)鍵參數(shù)和經(jīng)典算法。(2)提出一種基于鏈接壽命的社交網(wǎng)絡(luò)結(jié)構(gòu)分析方法。將鏈接壽命加入社交網(wǎng)絡(luò)結(jié)構(gòu)分析中,研究鏈接壽命對(duì)于社交網(wǎng)絡(luò)結(jié)構(gòu)中重要的基礎(chǔ)參數(shù)(包括度,網(wǎng)絡(luò)直徑和平均聚類系數(shù)等)的影響。實(shí)驗(yàn)表明,加入鏈接壽命后,社交網(wǎng)絡(luò)的演化結(jié)構(gòu)和傳統(tǒng)的研究有很大的不同,特別是,鏈接壽命的微小變化會(huì)導(dǎo)致網(wǎng)絡(luò)直徑的劇烈變化。(3)在上述社交網(wǎng)絡(luò)結(jié)構(gòu)的基礎(chǔ)上,提出了兩種半監(jiān)督的多標(biāo)簽分類算法。在兩種經(jīng)典的關(guān)系型分類器的基礎(chǔ)上,加入must-link約束和不確定性概率,研究must-link約束對(duì)于多標(biāo)簽分類的影響。實(shí)驗(yàn)表明,該方法在大規(guī)模社交網(wǎng)絡(luò)上比經(jīng)典關(guān)系型分類器具有更好的分類精度和效率,尤其當(dāng)已知標(biāo)簽數(shù)量很少的時(shí)候。(4)在上述算法計(jì)算得出的社會(huì)標(biāo)簽的基礎(chǔ)上,提出了一種多源評(píng)價(jià)聚合算法。首先基于評(píng)分者的社會(huì)標(biāo)簽計(jì)算他們的權(quán)威程度,然后將權(quán)威程度加入多源評(píng)價(jià)聚合過程中,來更加準(zhǔn)確的評(píng)估實(shí)體的真實(shí)得分。實(shí)驗(yàn)表明,該方法能有效消除推薦系統(tǒng)中的嚴(yán)格推薦者和寬松推薦者帶來的干擾噪音,并且無需任何關(guān)于嚴(yán)格和寬松推薦者比例的先驗(yàn)信息。
[Abstract]:With the rapid growth of social networks, social networking sites such as Facebook Twitter and YouTube have become successful with a large number of users. As a medium for sharing knowledge and interacting with friends, social networks play an increasingly important role in our lives. Label classification is an important application in social networks, such as users with interest tags and friends tags in social networks. Users can also tag text, pictures, and video messages on social networks. In traditional label classification, network data is represented by a single tag. However, with the abundance of various social network applications, the forms of network data are becoming more and more diverse. A single label can no longer satisfy the complex and multi-semantic characteristics of social network data. Therefore, more and more attention has been paid to the classification of multiple tags in the social network environment. Based on this, this paper will focus on three aspects: the analysis of social network structure, the classification of multi-label in social network environment and the application of multi-label in recommendation system. The main work of this paper is as follows: (1) the background and significance of multi-label classification in social network environment are introduced, and the research status and defects of social network structure analysis, multi-label classification and recommendation system are analyzed. The concepts, classification, key parameters and classical algorithms of related fields are also described in detail. (2) A social network structure analysis method based on link life is proposed. The link life is added to the analysis of the social network structure to study the influence of the link life on the important basic parameters (including degree, network diameter and average clustering coefficient) in the social network structure. The experimental results show that the evolutionary structure of social network is very different from the traditional research after adding link life, especially, the small change of link life will lead to the drastic change of network diameter. (3) based on the above social network structure, Two semi-supervised multi-label classification algorithms are proposed. On the basis of two classical relational classifiers, the influence of must-link constraints on multi-label classification is studied by adding must-link constraints and uncertainty probability. Experiments show that this method has better classification accuracy and efficiency than the classical relational classifier on large-scale social networks, especially when the number of known labels is small. (4) on the basis of the social labels calculated by the above algorithms, A multi-source evaluation aggregation algorithm is proposed. Firstly, based on the social label of the raters, the degree of authority is calculated, and then the degree of authority is added to the aggregation process of multi-source evaluation to evaluate the real score of the entity more accurately. Experiments show that the proposed method can effectively eliminate the interference noise caused by strict and loose referrals in the recommendation system and does not require any prior information about the proportion of strict and loose referrals.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09

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