融合多維簽到信息的LBSN鏈接預(yù)測(cè)研究
發(fā)布時(shí)間：2018-10-19 13:58
【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)技術(shù)的飛速發(fā)展,基于位置的服務(wù)不斷增加,越來(lái)越多的人通過(guò)在線社交網(wǎng)絡(luò)分享帶有地理標(biāo)記的圖片、視頻以及文本等內(nèi)容,形成了基于位置的社交網(wǎng)絡(luò)(Location Based social Network,LBSN)。對(duì)社交網(wǎng)絡(luò)進(jìn)行數(shù)據(jù)挖掘又稱為鏈接挖掘。本文研究的LBSN朋友關(guān)系鏈接預(yù)測(cè)是鏈接挖掘的一個(gè)分支,是當(dāng)下學(xué)者研究的熱點(diǎn)。對(duì)LBSN提供的大量基于時(shí)空維度的簽到信息進(jìn)行挖掘?yàn)殒溄宇A(yù)測(cè)研究提供新的方向。然而,LBSN用戶的簽到分布稀疏,且分析維度單一,對(duì)預(yù)測(cè)性能的改善造成困難。針對(duì)以上問(wèn)題,本文從用戶、時(shí)間、位置以及位置語(yǔ)義四個(gè)維度挖掘簽到信息中包含的用戶相似性特征,并利用有監(jiān)督學(xué)習(xí)的策略綜合這些特征進(jìn)行鏈接預(yù)測(cè)。在真實(shí)網(wǎng)絡(luò)數(shù)據(jù)集中的仿真實(shí)驗(yàn)結(jié)果表明,本文提出的方法顯著提高了鏈接預(yù)測(cè)的性能。論文的研究工作得到了國(guó)家自然科學(xué)基金項(xiàng)目(No.61172072、61271308)、北京市自然科學(xué)基金項(xiàng)目(No.4112045)和高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金(No.20100009110002)的支持。論文的主要工作和貢獻(xiàn)包括以下幾個(gè)方面:(1)從用戶、位置和時(shí)間三個(gè)維度來(lái)分析LBSN數(shù)據(jù)集基于簽到行為的分布特點(diǎn)。分析可知,LBSN用戶的簽到分布稀疏,這對(duì)充分利用簽到信息造成困難。(2)針對(duì)簽到地點(diǎn)分布稀疏的問(wèn)題,利用層次聚類算法對(duì)簽到地點(diǎn)進(jìn)行聚類,引入廣義地點(diǎn)的概念,并由此來(lái)構(gòu)建廣義的地點(diǎn)關(guān)系網(wǎng)絡(luò),從而大大減少網(wǎng)絡(luò)中的孤立點(diǎn)數(shù)目,盡可能的保留網(wǎng)絡(luò)中的用戶。針對(duì)用戶的簽到在時(shí)間維度分布稀疏的問(wèn)題,利用單個(gè)用戶在不同時(shí)刻簽到行為的相似性來(lái)修正兩個(gè)用戶在不同時(shí)刻簽到行為的相似性,充分利用簽到時(shí)間信息。(3)提出UTP模型來(lái)挖掘基于時(shí)空維度的用戶相似性特征,并提出了綜合用戶和位置的相似性特征和基于簽到時(shí)間的相似性特征。在真實(shí)網(wǎng)絡(luò)數(shù)據(jù)集中的驗(yàn)證表明,這兩個(gè)特征能夠有效區(qū)分朋友和非朋友關(guān)系。(4)從位置語(yǔ)義維度挖掘基于地點(diǎn)語(yǔ)義的用戶相似特征。利用LDA文檔主題建模思想對(duì)所有用戶的簽到語(yǔ)義POI信息進(jìn)行位置主題建模,并提出了基于簽到地點(diǎn)語(yǔ)義的用戶相似性特征。在真實(shí)網(wǎng)絡(luò)數(shù)據(jù)集中的驗(yàn)證表明,該特征能夠有效區(qū)分朋友和非朋友關(guān)系。(5)融合基于LBSN的網(wǎng)絡(luò)結(jié)構(gòu)信息、簽到地點(diǎn)信息以及地點(diǎn)語(yǔ)義信息得到多維相似性特征向量,并利用有監(jiān)督的策略來(lái)進(jìn)行鏈接預(yù)測(cè)。在真實(shí)網(wǎng)絡(luò)數(shù)據(jù)集中的實(shí)驗(yàn)表明,相較于傳統(tǒng)的鏈接預(yù)測(cè)算法,本文提出的基于多維信息的鏈接預(yù)測(cè)算法顯著提高了 LBSN鏈接預(yù)測(cè)的性能。
[Abstract]:With the rapid development of mobile Internet technology and the increasing number of location-based services, more and more people share geographically marked pictures, videos and text through online social networks. A location-based social network called (Location Based social Network,LBSN. Social network data mining, also known as link mining. In this paper, LBSN friend link prediction is a branch of link mining, which is a hot research topic. Mining a lot of sign-in information based on time and space dimension provided by LBSN provides a new direction for link prediction. However, the sparse check-in distribution of LBSN users and the single dimension of analysis make it difficult to improve the prediction performance. In order to solve the above problems, the user similarity features contained in the sign-in information are mined from four dimensions: user, time, location and location semantics, and these features are synthesized by supervised learning strategies for link prediction. Simulation results in real network data sets show that the proposed method improves the performance of link prediction significantly. The research work is supported by the National Natural Science Foundation (No.61172072,61271308), the Natural Science Foundation of Beijing (No.4112045) and the Special Research Foundation for doctorate points of higher Education (No.20100009110002). The main work and contributions of this paper are as follows: (1) the distribution characteristics of LBSN data sets based on check-in behavior are analyzed from three dimensions: user, location and time. The analysis shows that the LBSN user's check-in distribution is sparse, which makes it difficult to make full use of the check-in information. (2) aiming at the problem of sparse check-in location distribution, the hierarchical clustering algorithm is used to cluster the check-in location, and the concept of generalized location is introduced. Then the generalized location relationship network is constructed, which greatly reduces the number of outliers in the network and preserves the users in the network as much as possible. Aiming at the sparse distribution of user check-in time dimension, the similarity of check-in behavior of single user at different times is used to correct the similarity of check-in behavior between two users at different times. (3) UTP model is proposed to mine user similarity features based on spatio-temporal dimension, and the similarity features of integrated user and location and check-in time are proposed. Verification in real network data sets shows that the two features can effectively distinguish between friends and non-friends. (4) the location semantic dimension is used to mine the user similarity features based on location semantics. Based on the idea of LDA document topic modeling, the location topic of all users' check-in semantic POI information is modeled, and a user similarity feature based on check-in location semantics is proposed. Verification in real network data sets shows that the feature can effectively distinguish between friends and non-friends. (5) combining network structure information based on LBSN, check-in location information and location semantic information, multi-dimensional similarity feature vector is obtained. A supervised strategy is used for link prediction. Experiments in real network data sets show that the proposed link prediction algorithm based on multidimensional information improves the performance of LBSN link prediction significantly compared with the traditional link prediction algorithm.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.09;TP311.13
本文編號(hào)：2281341
[Abstract]:With the rapid development of mobile Internet technology and the increasing number of location-based services, more and more people share geographically marked pictures, videos and text through online social networks. A location-based social network called (Location Based social Network,LBSN. Social network data mining, also known as link mining. In this paper, LBSN friend link prediction is a branch of link mining, which is a hot research topic. Mining a lot of sign-in information based on time and space dimension provided by LBSN provides a new direction for link prediction. However, the sparse check-in distribution of LBSN users and the single dimension of analysis make it difficult to improve the prediction performance. In order to solve the above problems, the user similarity features contained in the sign-in information are mined from four dimensions: user, time, location and location semantics, and these features are synthesized by supervised learning strategies for link prediction. Simulation results in real network data sets show that the proposed method improves the performance of link prediction significantly. The research work is supported by the National Natural Science Foundation (No.61172072,61271308), the Natural Science Foundation of Beijing (No.4112045) and the Special Research Foundation for doctorate points of higher Education (No.20100009110002). The main work and contributions of this paper are as follows: (1) the distribution characteristics of LBSN data sets based on check-in behavior are analyzed from three dimensions: user, location and time. The analysis shows that the LBSN user's check-in distribution is sparse, which makes it difficult to make full use of the check-in information. (2) aiming at the problem of sparse check-in location distribution, the hierarchical clustering algorithm is used to cluster the check-in location, and the concept of generalized location is introduced. Then the generalized location relationship network is constructed, which greatly reduces the number of outliers in the network and preserves the users in the network as much as possible. Aiming at the sparse distribution of user check-in time dimension, the similarity of check-in behavior of single user at different times is used to correct the similarity of check-in behavior between two users at different times. (3) UTP model is proposed to mine user similarity features based on spatio-temporal dimension, and the similarity features of integrated user and location and check-in time are proposed. Verification in real network data sets shows that the two features can effectively distinguish between friends and non-friends. (4) the location semantic dimension is used to mine the user similarity features based on location semantics. Based on the idea of LDA document topic modeling, the location topic of all users' check-in semantic POI information is modeled, and a user similarity feature based on check-in location semantics is proposed. Verification in real network data sets shows that the feature can effectively distinguish between friends and non-friends. (5) combining network structure information based on LBSN, check-in location information and location semantic information, multi-dimensional similarity feature vector is obtained. A supervised strategy is used for link prediction. Experiments in real network data sets show that the proposed link prediction algorithm based on multidimensional information improves the performance of LBSN link prediction significantly compared with the traditional link prediction algorithm.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.09;TP311.13
【參考文獻(xiàn)】
相關(guān)期刊論文 前8條
1 李宏濤;何克清;王健;彭珍連;田剛;;基于概念格和隨機(jī)游走的社交網(wǎng)朋友推薦算法[J];四川大學(xué)學(xué)報(bào)(工程科學(xué)版);2015年06期
2 王瑩;郭宇春;;基于位置的社交網(wǎng)絡(luò)鏈接預(yù)測(cè)特征研究[J];計(jì)算機(jī)與現(xiàn)代化;2015年04期
3 WANG Peng;XU BaoWen;WU YuRong;ZHOU XiaoYu;;Link prediction in social networks: the state-of-the-art[J];Science China(Information Sciences);2015年01期
4 盧文羊;徐佳一;楊育彬;;基于LDA主題模型的社會(huì)網(wǎng)絡(luò)鏈接預(yù)測(cè)[J];山東大學(xué)學(xué)報(bào)(工學(xué)版);2014年06期
5 張健沛;姜延良;;一種基于節(jié)點(diǎn)相似性的鏈接預(yù)測(cè)算法[J];中國(guó)科技論文;2013年07期
6 呂琳媛;;復(fù)雜網(wǎng)絡(luò)鏈路預(yù)測(cè)[J];電子科技大學(xué)學(xué)報(bào);2010年05期
7 趙慧;劉希玉;崔海青;;網(wǎng)格聚類算法[J];計(jì)算機(jī)技術(shù)與發(fā)展;2010年09期
8 唐華松,姚耀文;數(shù)據(jù)挖掘中決策樹(shù)算法的探討[J];計(jì)算機(jī)應(yīng)用研究;2001年08期
相關(guān)博士學(xué)位論文 前1條
1 蔣良孝;樸素貝葉斯分類器及其改進(jìn)算法研究[D];中國(guó)地質(zhì)大學(xué);2009年
相關(guān)碩士學(xué)位論文 前5條
1 吳曉陽(yáng);微博用戶社會(huì)關(guān)系離線挖掘算法的研究[D];北京交通大學(xué);2016年
2 王瑩;基于位置的社交網(wǎng)絡(luò)鏈接預(yù)測(cè)系統(tǒng)研究[D];北京交通大學(xué);2015年
3 朱榮鑫;基于地理位置的社交網(wǎng)絡(luò)潛在用戶和位置推薦模型研究[D];南京郵電大學(xué);2013年
4 補(bǔ)嘉;基于LDA的社交網(wǎng)絡(luò)鏈接預(yù)測(cè)模型研究[D];西南大學(xué);2012年
5 郭宏偉;基于矩陣的多特征鏈接預(yù)測(cè)方法研究[D];燕山大學(xué);2010年
，本文編號(hào)：2281341
本文鏈接：http://www.sikaile.net/guanlilunwen/ydhl/2281341.html
最近更新
教材專著
