社交網(wǎng)絡(luò)中基于用戶特征的專家推薦研究
發(fā)布時(shí)間：2018-08-23 09:10
【摘要】：網(wǎng)絡(luò)社交平臺經(jīng)過數(shù)年蓬勃發(fā)展帶動(dòng)了大量用戶參與,網(wǎng)民通過該平臺與各個(gè)層面的人聯(lián)系在一起,在這一過程中由微博發(fā)布、轉(zhuǎn)發(fā)以及評論產(chǎn)生互動(dòng)而形成了新的巨大信息流。這給信息獲取帶來便利的同時(shí)也不可避免地把信息過載這一難題推向前臺,所以通過信息過濾這個(gè)手段進(jìn)行個(gè)性化推薦具有重大價(jià)值。在擁有海量數(shù)據(jù)和用戶的社交網(wǎng)絡(luò)里進(jìn)行信息過濾難度巨大,其中一個(gè)重要因素就在于社交網(wǎng)絡(luò)信息發(fā)布門檻低,容易造成信息質(zhì)量魚龍混雜的現(xiàn)象。與此同時(shí)現(xiàn)有技術(shù)在分辨各個(gè)領(lǐng)域信息的質(zhì)量高低、真假等屬性的性能上還有改進(jìn)的空間,因此我們可以在改進(jìn)算法的同時(shí)借助特定領(lǐng)域的專家,依靠其專業(yè)知識和技能,幫助用戶進(jìn)行信息的篩選。為了達(dá)成這一目標(biāo)本研究主要從用戶的特征入手,依據(jù)其興趣推薦個(gè)性化的專家。社交網(wǎng)絡(luò)用戶往往都有一些用于描述自己特征的標(biāo)簽,通過該標(biāo)簽可以快速且準(zhǔn)確地識別用戶興趣。然而在社交網(wǎng)絡(luò)里由于使用門檻、隱私保護(hù)等因素的限制,用戶的個(gè)人標(biāo)簽往往不夠普及,因此該特征信息稀疏,從而造成推薦困難。為解決這一問題,根據(jù)同質(zhì)性,即相似的用戶會喜歡相似的內(nèi)容,借用用戶親密好友的特征進(jìn)行標(biāo)簽預(yù)測。本文首先使用基于SimRank的改進(jìn)算法ASCOS對用戶社交關(guān)系相似度進(jìn)行計(jì)算,然后進(jìn)行兩兩比較找出用戶的親密好友,再根據(jù)其好友標(biāo)簽進(jìn)行預(yù)測。隨后在專家識別方面提出了依據(jù)PageRank為原型的FRank算法,改進(jìn)了原始算法在小社交圈內(nèi)計(jì)算不準(zhǔn)的缺陷。實(shí)證表明,使用ASCOS在用戶標(biāo)簽預(yù)測中的準(zhǔn)確率和召回率上得到提升。在專家預(yù)測上使用nDGC作為性能評價(jià)標(biāo)準(zhǔn),并發(fā)現(xiàn)與基線方法PageRank相比FRank的性能也有所提升。最終依據(jù)上述兩個(gè)方面的成果,即標(biāo)簽預(yù)測和專家識別,實(shí)現(xiàn)了向用戶推薦個(gè)性化的專家。本文共分6章:第1章,介紹當(dāng)前社交網(wǎng)絡(luò)個(gè)性化推薦的研究現(xiàn)狀和成果,描述文章結(jié)構(gòu)。第2章,說明本文研究所需的理論技術(shù),包括數(shù)據(jù)采集的方法、用戶標(biāo)簽、用戶特征模型及常見推薦系統(tǒng)。第3章,根據(jù)新浪微博用戶的宏觀特點(diǎn)選擇用戶標(biāo)簽作為推薦模型的基礎(chǔ),在此基礎(chǔ)之上使用ASCOS算法計(jì)算用戶相似度并對用戶標(biāo)簽進(jìn)行擴(kuò)展。第4章,通過預(yù)測標(biāo)簽和社交關(guān)系網(wǎng)絡(luò),使用FRank算法對該網(wǎng)絡(luò)中的專家用戶進(jìn)行識別,并完成推薦系統(tǒng)設(shè)計(jì)。第5章,對新浪微博的數(shù)據(jù)進(jìn)行分析,得到標(biāo)簽預(yù)測和專家識別的結(jié)果,通過實(shí)證驗(yàn)證本研究的有效性。第6章,總結(jié)分析結(jié)果,找出本研究所存在改進(jìn)的空間,為下一步的探尋做好鋪墊。
[Abstract]:After several years of vigorous development, the social networking platform has brought a large number of users to participate, through which Internet users connect with people at all levels, and in the process, they are released by Weibo. The interaction between retweets and comments creates a huge new flow of information. This brings convenience to information acquisition, but also inevitably brings the problem of information overload to the foreground, so it is of great value to carry out personalized recommendation through information filtering. It is very difficult to filter information in social networks with huge data and users. One of the important factors is the low threshold of information release on social networks, which can easily lead to mixed information quality. At the same time, there is still room for improvement in the existing technology in terms of distinguishing the quality of information in various fields and the performance of attributes such as truth and falsehood, so we can improve our algorithms while relying on the expertise and skills of experts in specific fields. Help users filter information. In order to achieve this goal, this study mainly starts with the characteristics of users and recommends personalized experts according to their interests. Social network users often have tags to describe their own characteristics, which can quickly and accurately identify user interests. However, due to the restrictions of threshold and privacy protection in social networks, the user's personal tags are often not popular enough, so the feature information is sparse, resulting in the difficulty of recommendation. In order to solve this problem, according to homogeneity, that is, similar users will like similar content, and use the characteristics of close friends to predict the labels. In this paper, we first use the improved algorithm ASCOS based on SimRank to calculate the similarity of users' social relations, and then compare and find out the close friends of users, and then predict them according to their friend tags. Then the FRank algorithm based on PageRank is put forward in the aspect of expert recognition, which improves the defects of the original algorithm in the small social circle. The empirical results show that the accuracy and recall rate of user label prediction are improved by using ASCOS. In the expert prediction, nDGC is used as the performance evaluation standard, and the performance of FRank is also improved compared with the baseline method PageRank. Finally, according to the results of above two aspects, label prediction and expert recognition, personalized experts are recommended to users. This paper is divided into six chapters: chapter 1 introduces the current research status and achievements of personalized recommendation of social networks, describes the structure of the article. In chapter 2, the theory and technology needed in this paper are described, including data acquisition method, user label, user feature model and common recommendation system. In chapter 3, according to the macro characteristics of Sina Weibo users, the user tags are selected as the basis of the recommendation model, and the ASCOS algorithm is used to calculate the user similarity and extend the user tags. In chapter 4, the expert users in the network are identified by using the FRank algorithm and the recommendation system is designed by using the prediction label and the social relationship network. In chapter 5, the data of Sina Weibo are analyzed, the results of label prediction and expert identification are obtained, and the validity of this study is verified by empirical analysis. Chapter 6, summing up the analysis results, find out the room for improvement in this research, and pave the way for the next step.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：G252;G206
本文編號：2198581
[Abstract]:After several years of vigorous development, the social networking platform has brought a large number of users to participate, through which Internet users connect with people at all levels, and in the process, they are released by Weibo. The interaction between retweets and comments creates a huge new flow of information. This brings convenience to information acquisition, but also inevitably brings the problem of information overload to the foreground, so it is of great value to carry out personalized recommendation through information filtering. It is very difficult to filter information in social networks with huge data and users. One of the important factors is the low threshold of information release on social networks, which can easily lead to mixed information quality. At the same time, there is still room for improvement in the existing technology in terms of distinguishing the quality of information in various fields and the performance of attributes such as truth and falsehood, so we can improve our algorithms while relying on the expertise and skills of experts in specific fields. Help users filter information. In order to achieve this goal, this study mainly starts with the characteristics of users and recommends personalized experts according to their interests. Social network users often have tags to describe their own characteristics, which can quickly and accurately identify user interests. However, due to the restrictions of threshold and privacy protection in social networks, the user's personal tags are often not popular enough, so the feature information is sparse, resulting in the difficulty of recommendation. In order to solve this problem, according to homogeneity, that is, similar users will like similar content, and use the characteristics of close friends to predict the labels. In this paper, we first use the improved algorithm ASCOS based on SimRank to calculate the similarity of users' social relations, and then compare and find out the close friends of users, and then predict them according to their friend tags. Then the FRank algorithm based on PageRank is put forward in the aspect of expert recognition, which improves the defects of the original algorithm in the small social circle. The empirical results show that the accuracy and recall rate of user label prediction are improved by using ASCOS. In the expert prediction, nDGC is used as the performance evaluation standard, and the performance of FRank is also improved compared with the baseline method PageRank. Finally, according to the results of above two aspects, label prediction and expert recognition, personalized experts are recommended to users. This paper is divided into six chapters: chapter 1 introduces the current research status and achievements of personalized recommendation of social networks, describes the structure of the article. In chapter 2, the theory and technology needed in this paper are described, including data acquisition method, user label, user feature model and common recommendation system. In chapter 3, according to the macro characteristics of Sina Weibo users, the user tags are selected as the basis of the recommendation model, and the ASCOS algorithm is used to calculate the user similarity and extend the user tags. In chapter 4, the expert users in the network are identified by using the FRank algorithm and the recommendation system is designed by using the prediction label and the social relationship network. In chapter 5, the data of Sina Weibo are analyzed, the results of label prediction and expert identification are obtained, and the validity of this study is verified by empirical analysis. Chapter 6, summing up the analysis results, find out the room for improvement in this research, and pave the way for the next step.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：G252;G206
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