基于用户关系分析和微博内容挖掘的信息推荐系统研究
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摘要
随着Web2.O时代的到来,社交网络服务飞速发展,它通过网络这一新型载体将各类人群连接起来,对人们的信息获得和生活方式产生了不可低估的影响。近两年来,微博作为一种新兴的社交网络平台走入大众视野,并且迅速以其新颖便捷的信息传播模式广受各类用户青睐。快速增长的用户数量也导致信息总量的爆炸式增长,如何从海量的微博信息中提取用户感兴趣的话题并推荐给用户,已然成为一个急需解决的研究问题。本论文基于用户关系分析和微博内容挖掘进行信息推荐,目的在于向微博用户提供个性化服务。
论文的主要工作包括：
1.通过对微博用户关系的分析,提出对中心用户有影响的相关用户影响力的算法。本文通过新浪微博用户数据,分析用户关系网络的形成原因,结合模糊综合评价法,确定影响用户影响力的因素间的关系,并提出用户影响力的计算公式,构建中心用户的用户影响力模型。
2.通过对于微博内容的分析,提出基于话题级别的微博研究方法。本文结合可信关联规则对相关用户的微博进行话题检测,并且基于词激活力相关概念对相关用户的情感倾向性进行分析,以确定某一相关用户对于某一话题的情感倾向性。
3.结合前两部分内容,提出微博信息的推荐算法。本文对已有的信息推荐算法进行回顾,并归纳总结了微博信息推荐的方法。在此基础上,提出基于用户关系和微博内容分析,向中心用户进行微博推荐的计算方法,并通过实验验证了算法的有效性。
本论文基于用户关系和微博内容进行微博信息推荐,可以更好的进行个性化用户服务,.良好的用户体验性能够增强微博用户的满意度,同时能够为舆情的监控等提供理论依据,具有理论和应用的双重价值。
With the coming era of web2.0, social network is developing rapidly. It gathers various kinds of people together with the help of internet, the new style of carrier, which has a significant impact on the information obtaining methods and life styles of individuals. In the recent two years, micro-blog, as a novel social network platform, appears in the sights of people, and it is widely popular in various kinds of users because of its novel and convenient model of information propagation. The rapid growth of the user number also leads to the explosive growth of the amount of the information. Therefore, how to extract the topics from massive amounts of information, which the user is interested in, and recommend the corresponding topics to the user is a research problem demanding to be solved immediately. This thesis is based on analysis of user relationship and mining of micro-blog contents to recommend information, and the purpose is to provide personalized service. The main work of this thesis includes:
1. With analysis of user relationship, this thesis proposes an algorithm to detect influence value of related users to the core user. With the SINA micro-blog user data, this thesis analyzes the reason of formation of user relationship network. Combining with the fuzzy comprehensive evaluation method, we can confirm the relationships between factors which impact the influence value of users. And we propose a formula for calculating the influence value of user to build the model of user influence value for the core user..
2. With analysis of micro-blog contents, this thesis proposes the research method standing on the level of topics of micro-blogs. Analysis of micro-blog contents. The thesis combines the credible association rules to detect the topics of related users. At the same time, we use theory of word activation forces to analyze the emotional tendentiousness of related user to identify his or her sentiment about a specific topic.
3. Combining with the previous sections, this thesis provides a recommendation algorithm for micro-blog information. This thesis reviews some existing theories about information recommendation, and summarizes the recommendation methods of micro-blog information. Moreover, we propose an algorithm based on the analysis of user relationship and micro-blog contents, which can help to recommend micro-blogs to the core users and we prove it with experiment. This thesis is based on user relationship analysis and micro-blogs
contents mining to recommend information, which can help to provide
personalized service. Good user experience could help to increase the
satisfaction of the user, and it can provide theory basis for public opinions
monitoring, which has both theoretical value and practical value.
论文的主要工作包括：
1.通过对微博用户关系的分析,提出对中心用户有影响的相关用户影响力的算法。本文通过新浪微博用户数据,分析用户关系网络的形成原因,结合模糊综合评价法,确定影响用户影响力的因素间的关系,并提出用户影响力的计算公式,构建中心用户的用户影响力模型。
2.通过对于微博内容的分析,提出基于话题级别的微博研究方法。本文结合可信关联规则对相关用户的微博进行话题检测,并且基于词激活力相关概念对相关用户的情感倾向性进行分析,以确定某一相关用户对于某一话题的情感倾向性。
3.结合前两部分内容,提出微博信息的推荐算法。本文对已有的信息推荐算法进行回顾,并归纳总结了微博信息推荐的方法。在此基础上,提出基于用户关系和微博内容分析,向中心用户进行微博推荐的计算方法,并通过实验验证了算法的有效性。
本论文基于用户关系和微博内容进行微博信息推荐,可以更好的进行个性化用户服务,.良好的用户体验性能够增强微博用户的满意度,同时能够为舆情的监控等提供理论依据,具有理论和应用的双重价值。
With the coming era of web2.0, social network is developing rapidly. It gathers various kinds of people together with the help of internet, the new style of carrier, which has a significant impact on the information obtaining methods and life styles of individuals. In the recent two years, micro-blog, as a novel social network platform, appears in the sights of people, and it is widely popular in various kinds of users because of its novel and convenient model of information propagation. The rapid growth of the user number also leads to the explosive growth of the amount of the information. Therefore, how to extract the topics from massive amounts of information, which the user is interested in, and recommend the corresponding topics to the user is a research problem demanding to be solved immediately. This thesis is based on analysis of user relationship and mining of micro-blog contents to recommend information, and the purpose is to provide personalized service. The main work of this thesis includes:
1. With analysis of user relationship, this thesis proposes an algorithm to detect influence value of related users to the core user. With the SINA micro-blog user data, this thesis analyzes the reason of formation of user relationship network. Combining with the fuzzy comprehensive evaluation method, we can confirm the relationships between factors which impact the influence value of users. And we propose a formula for calculating the influence value of user to build the model of user influence value for the core user..
2. With analysis of micro-blog contents, this thesis proposes the research method standing on the level of topics of micro-blogs. Analysis of micro-blog contents. The thesis combines the credible association rules to detect the topics of related users. At the same time, we use theory of word activation forces to analyze the emotional tendentiousness of related user to identify his or her sentiment about a specific topic.
3. Combining with the previous sections, this thesis provides a recommendation algorithm for micro-blog information. This thesis reviews some existing theories about information recommendation, and summarizes the recommendation methods of micro-blog information. Moreover, we propose an algorithm based on the analysis of user relationship and micro-blog contents, which can help to recommend micro-blogs to the core users and we prove it with experiment. This thesis is based on user relationship analysis and micro-blogs
contents mining to recommend information, which can help to provide
personalized service. Good user experience could help to increase the
satisfaction of the user, and it can provide theory basis for public opinions
monitoring, which has both theoretical value and practical value.
引文
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[4]张钰雪,新浪微博传播机制研究——以“围脖女王”姚晨微博为例[D].重庆.西南大学.2011.
[5]微博,百度百科http://baike.baidu.com/view/1567099.htm
[6]孙胜平,中文微博客热点话题检测与跟踪技术研究[D].北京交通大学.2011.
[7]王杨.微型博客的发展前景及影响分析[J].今传媒.2011,3.pp：100-103
[8]Kang J H,Lermank,Plangprasopchok A.Analyzing.Microblogs with affinity Propagation[C]//Proc of the 1stKDD workshop on Social Media Analytic.New Yourk:ACM,2010:67-70.
[9]张晨逸,孙建伶,丁轶群.基于MB-LDA模型的微博主题挖掘[J].计算机研究与发展.2011,10.pp：1795-1802
[10]张琳,我国微博的发展研究——以新浪微博为例[D].江西财经大学,2012
[11]http://help.weibo.com/faq/q/1201
[12]李爽,从微博中挖掘有用信息[J].网络与信息.2011.pp：6-8.
[13]平亮,宗利永.基于社会网络中心性分析的微博信息传播研究——以Sina微博为例[J].图书情报知识.2010.6.pp：92-97.
[14]王晓光.微博客用户行为特征与关系特征实证分析——以“新浪微博”为例[J].竞争情报.2010,54(14).pp：66-70.
[15]何黎,何跃,霍叶青.微博用户特征分析和核心用户挖掘[J].信息系统.2011,34(11).pp：121-125.
[16]郭浩,陆余良,王宇等.基于信息传播的微博用户影响力度量[J].山东大学学(理学版).2012,5-47(5).pp：78-83.
[17]刘建.新浪微博用户体验及传播影响力分析[J].出版参考.2011.12(2).pp：17-18.
[18]赵新.浅析名人微博影响舆论的原理与模式[J].现代视听.2011.8.pp：64-66.
[19]杨亮,林原,林鸿飞.基于情感分布的微博热点事件发现[J].中文信息学报.2012,139(1).pp：84-90.
[20]郑斐然,苗多谦,张志飞等.一种中文微博新闻话题检测的方法[J].计算机科学2012,1-39(1).pp：138-141.
[21]杨冠超.浙江大学微博客热点话题发现策略研究[D].浙江.浙江大学.2011.
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