本文選題：關(guān)聯(lián)規(guī)則　切入點：相似度　出處：《新疆大學(xué)》2017年碩士論文

【摘要】：隨著現(xiàn)實人類社會與虛擬網(wǎng)絡(luò)融合的不斷深化,新興的社會網(wǎng)絡(luò)為信息的快速傳播和交流共享提供了便捷有效的平臺。這使得社會網(wǎng)絡(luò)中的用戶能隨時了解,并參與信息的交互過程。人們在信息交互環(huán)境中所留下的數(shù)字足跡形成了各種各樣的關(guān)系網(wǎng)絡(luò)。由于網(wǎng)絡(luò)中用戶關(guān)系數(shù)據(jù)具有潛在的商業(yè)及研究價值,社會網(wǎng)絡(luò)中社區(qū)劃分及用戶推薦的相關(guān)研究已成為目前研究人員廣泛關(guān)注的課題。本文主要研究了關(guān)聯(lián)規(guī)則算法、基于線圖的重疊社區(qū)發(fā)現(xiàn)在微博中的應(yīng)用以及用戶推薦算法。具體工作內(nèi)容和研究成果如下:1.對于傳統(tǒng)Apriori算法存在時間復(fù)雜度較高,無關(guān)冗余項較多的問題,提出基于位圖數(shù)據(jù)和散列函數(shù)的BHA算法,結(jié)合相關(guān)性質(zhì),有效提升了算法的計算效率。同時,在基于微博用戶的推薦應(yīng)用中,由關(guān)注關(guān)系計算用戶間的出,入相似度,并引入信任度概念,彌補了相似度未考慮用戶間距離的問題。在此基礎(chǔ)上,結(jié)合BHA算法進行用戶推薦�；谖⒉┯脩魯�(shù)據(jù)的實驗結(jié)果表明,關(guān)聯(lián)規(guī)則挖掘效率及算法推薦的有效性均有所提高。2.提出了基于用戶關(guān)注相似和標(biāo)簽相似的微博社區(qū)發(fā)現(xiàn)網(wǎng)絡(luò)模型,將CNM算法與該模型相結(jié)合進行微博社區(qū)發(fā)現(xiàn)。對于檢測結(jié)果中存在的節(jié)點過重疊現(xiàn)象,引入核心鏈路概念,對網(wǎng)絡(luò)中的孤立邊進行刪除,有效解決了節(jié)點的過度重疊問題。在此基礎(chǔ)上,進一步分析共鄰好友間的關(guān)系,提出了基于社區(qū)檢測的微博用戶推薦算法。將所提算法應(yīng)用于真實網(wǎng)絡(luò)和微博網(wǎng)絡(luò),實驗表明社區(qū)發(fā)現(xiàn)的精確度得到進一步提高,同時,將社區(qū)檢測與用戶推薦相結(jié)合有效提升了推薦結(jié)果的準(zhǔn)確性。
[Abstract]:With the deepening of the integration of real human society and virtual network, the emerging social network provides a convenient and effective platform for the rapid dissemination and exchange and sharing of information, which enables the users in the social network to understand it at any time. And participate in the process of information interaction. The digital footprint left by people in the information interactive environment forms a variety of relational networks. Because of the potential commercial and research value of user relationship data in the network, The research on community division and user recommendation in social network has become a topic that researchers pay more attention to. This paper mainly studies the algorithm of association rules. The application of line graph based overlapping community discovery and user recommendation algorithm in Weibo. The specific work and research results are as follows: 1.The traditional Apriori algorithm has the problems of higher time complexity and more irrelevant redundant items. This paper proposes a BHA algorithm based on bitmap data and hash function, which combines the related properties and improves the efficiency of the algorithm. At the same time, in the recommended application of Weibo, the similarity between users is calculated by the concern relation. The concept of trust is introduced to make up for the problem that the similarity does not take into account the distance between users. On this basis, the BHA algorithm is used to recommend users. The experimental results based on Weibo user data show that, The efficiency of association rules mining and the effectiveness of algorithm recommendation are improved. 2. Weibo community discovery network model based on user concern similarity and label similarity is proposed. The CNM algorithm is combined with the model for Weibo community discovery. The concept of core link is introduced to remove the isolated edges in the network. The problem of excessive overlap of nodes is effectively solved. On this basis, the relationship between common neighbors and friends is further analyzed, and a recommendation algorithm for Weibo users based on community detection is proposed. The proposed algorithm is applied to real networks and Weibo networks. The experimental results show that the accuracy of community discovery is further improved, and the accuracy of recommended results is improved effectively by combining community detection with user recommendation.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O157.5;TP391.3

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