用户移动端与社交端行为建模与模式分析
发布时间：2018-10-31 11:37
【摘要】：随着互联网技术的迅猛发展,各种服务商在互联网中开启的流量入口也越来越多,形式各不相同,硬件上,用户可以通过PC、智能平板、手机等使用相关服务,在软件上也包含了如社交网络、电子商务、游戏等各类应用渠道。也就是说,在现今的互联网大环境下,有很多种渠道可以留下用户的行为轨迹,这些行为轨迹能从某种程度上能够折射出用户的个性化特征,通过挖掘这些个性化特征可以帮助我们了解用户日常的行为习惯,并对用户进行较为准确的服务推荐。用户在线使用服务的行为一般在两种场景下发生,一种是围绕用户个人产生的行为,只是满足自己的需求,并不与他人发生直接关系,比如在线音乐,手机阅读等,另一种是多个用户互相协作以满足某种需求,比如社交网络。那么我们针对这两种场景展开对用户在线行为的研究,即用户在移动端的行为与用户在社交端的行为,通过对行为的建模与行为模式的分析来研究用户使用服务中的行为习惯。对于用户在移动端的行为研究,我们挑选安卓用户作为我们的研究群体,并进行了如下工作:通过手机程序采集用户在移动端发生的行为以上下文信息,并对数据进行预处理;提出四种基于上下文的用户的行为模式;设计挖掘基于上下文的行为模式算法并进行算法的评价;提出若干移动端用户行为预测策略;通过可视化系统来展示挖掘结果。对于用户在社交端的行为研究,因为每种社交平台给用户提供的行为类型都不同,所以我们挑选行为类型较多,用户活跃度较高的这种比较代表性的社交协作平台github来进行研究,并进行了如下工作:通过信息抓取等技术采集用户在github的行为并对数据进行预处理;对用户在线行为特征进行分析;提出两种行为模式:面向具体开发者的行为模式(PP)与面向抽象开发者的行为模式(RP);设计社交端用户行为模式挖掘算法并实证分析;提出若干社交端用户行为预测策略;通过可视化系统来展示挖掘结果。研究结果发现,用户在线行为是遵循一定的行为模式,用户在移动端使用以个人为中心的服务时,与上下文结合可以更好地解释用户的行为模式,而用户在社交端与其他用户协作时,用户间的模式之间差异与共性并存。
[Abstract]:With the rapid development of Internet technology, various service providers open more and more traffic ports in the Internet in different forms. In hardware, users can use related services through PC, smart tablets, mobile phones, etc. Software also includes various application channels such as social networks, e-commerce, games and so on. That is to say, in today's Internet environment, there are many channels that can leave the user's behavior track, which can to some extent reflect the personalized characteristics of the user. Mining these personalized features can help us to understand the user's daily behavior habits and make a more accurate service recommendation to the user. The behavior of users using services online generally takes place in two scenarios. One is the behavior around the user, which only meets his own needs and does not have a direct relationship with others, such as online music, cell phone reading, and so on. The other is the collaboration of multiple users to meet certain needs, such as social networks. Then we study the online behavior of users in these two scenarios, that is, the behavior of users on the mobile side and the behavior of users on the social side, and the behavior habits of users in the use of services are studied through the modeling of behavior and the analysis of behavior patterns. For the research of users' behavior on mobile side, we select Android users as our research group, and do the following work: collect the behavior of users on mobile side with context information through mobile phone program, and preprocess the data; Four context-based user behavior patterns are proposed; context-based behavior pattern algorithms are designed and evaluated; several mobile user behavior prediction strategies are proposed; and mining results are displayed through a visual system. In terms of the behavior of users on the social side, because each social platform provides users with different types of behavior, we choose more types of behavior. Github, a representative social cooperation platform with high user activity, is studied. The following works are done: collecting the behavior of users in github and preprocessing the data through information capture and other technologies; This paper analyzes the characteristics of users' online behavior, proposes two kinds of behavior patterns: (PP) for specific developers and (RP); for abstract developers, designs and empirically analyzes the algorithm of social user behavior pattern mining. Several social user behavior prediction strategies are proposed, and the mining results are displayed through a visualization system. The results show that the online behavior of the user follows a certain behavior pattern. When the user uses a personal-centric service on the mobile side, it can better explain the behavior pattern of the user by combining with the context. When users cooperate with other users on the social side, the differences and commonalities exist among users.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP311.13
，
本文编号：2302039
[Abstract]:With the rapid development of Internet technology, various service providers open more and more traffic ports in the Internet in different forms. In hardware, users can use related services through PC, smart tablets, mobile phones, etc. Software also includes various application channels such as social networks, e-commerce, games and so on. That is to say, in today's Internet environment, there are many channels that can leave the user's behavior track, which can to some extent reflect the personalized characteristics of the user. Mining these personalized features can help us to understand the user's daily behavior habits and make a more accurate service recommendation to the user. The behavior of users using services online generally takes place in two scenarios. One is the behavior around the user, which only meets his own needs and does not have a direct relationship with others, such as online music, cell phone reading, and so on. The other is the collaboration of multiple users to meet certain needs, such as social networks. Then we study the online behavior of users in these two scenarios, that is, the behavior of users on the mobile side and the behavior of users on the social side, and the behavior habits of users in the use of services are studied through the modeling of behavior and the analysis of behavior patterns. For the research of users' behavior on mobile side, we select Android users as our research group, and do the following work: collect the behavior of users on mobile side with context information through mobile phone program, and preprocess the data; Four context-based user behavior patterns are proposed; context-based behavior pattern algorithms are designed and evaluated; several mobile user behavior prediction strategies are proposed; and mining results are displayed through a visual system. In terms of the behavior of users on the social side, because each social platform provides users with different types of behavior, we choose more types of behavior. Github, a representative social cooperation platform with high user activity, is studied. The following works are done: collecting the behavior of users in github and preprocessing the data through information capture and other technologies; This paper analyzes the characteristics of users' online behavior, proposes two kinds of behavior patterns: (PP) for specific developers and (RP); for abstract developers, designs and empirically analyzes the algorithm of social user behavior pattern mining. Several social user behavior prediction strategies are proposed, and the mining results are displayed through a visualization system. The results show that the online behavior of the user follows a certain behavior pattern. When the user uses a personal-centric service on the mobile side, it can better explain the behavior pattern of the user by combining with the context. When users cooperate with other users on the social side, the differences and commonalities exist among users.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP311.13
，
本文编号：2302039
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