移动个性化信息服务系统的进化机制研究
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摘要
在互联网、通信网等媒介成为重要生活工具的同时,信息过载(Information Overload)问题也随之而来。个性化信息服务系统旨在为用户提供满足其个性化偏好的信息服务,有效解决了该问题。当前,移动计算(Mobile computing)为个性化服务系统带来了新的挑战,传统机制从用户反馈中提取用户兴趣,通过逐步调整用户模型(User model)的内容与服务机制的参数来修正服务内容,该机制在置于移动信息服务领域中时,效果并不理想。因为,移动信息服务将多种复杂因素引入了个性化服务问题,提高了问题的复杂性。其中,“移动用户的个性化特征明显增多”是关键问题,它使得原本就难以把握的用户兴趣变化具有了大幅度、随机性和跳跃性等特点,使个性化服务面临着快速衍生新业务的压力。然而,传统个性化服务机制滞后式的学习方法使系统不具备灵活应对用户兴趣变化的能力。由于传统机制很少考虑用户兴趣变化的规律,系统不具备主动进化(Evolution)以快速迎接用户兴趣变化的能力。
本文旨在弥补传统个性化服务机制在移动领域表现出的上述不足,致力于研究移动个性化信息服务系统的进化机制,以实现如下愿景：系统能够自动进化其服务内容、组织结构和界面外观以适应移动用户多变的兴趣。并且,进化机制具备工程实现的可行性。基于此想法,本文研究内容包括四方面：移动用户兴趣变化的驱动因素与规律研究、移动个性化系统进化机制研究、面向数据流的用户兴趣数据挖掘研究、软件进化(Software evolution)方法学研究。本文所做的主要工作和创新点如下：
(1)针对移动个性化信息服务难以有效适应用户需求变化的问题,提出了一种面向行为动机的个性化信息服务模型,通过使用隐马尔科夫模型建模用户的兴趣变化,一定程度上解决了大跨度的用户兴趣变化预测问题,并借此改进了个性化服务的质量；针对无法准确度量用户行为模式兴趣度的问题,通过引入用户信息获取行为(Information Seeking Behavior)规律,提出了一种基于关联规则预期支持度的兴趣度度量方法,提高了关联规则兴趣度的预测准确度；针对难以预测用户移动规律的问题,通过引入空间认知,提出了基于空间认知分析的用户移动规律预测方法,提高了预测准确度。
(2)研究了移动个性化信息服务系统进化机制,提出了一种针对Web页面的布局优化方法；针对需要为不同用户维持不同版本的站点内容而增加服务器负担的问题,提出了一种个性化页面标签转换与内容自动生成机制,能够为用户动态生成个性化的服务内容；针对移动终端计算能力低,无法执行机器学习算法以提取用户兴趣的问题,提出了一种具有智能性质的客户端程序自动配置方法,该方法可以在较短时间内计算用户个性化配置偏好,并在适当上下文环境中进行配置推荐。
(3)研究了用户兴趣数据的挖掘技术,针对需要从快速到达后台系统的用户信息行为数据流中尽快挖掘到用户行为规律的问题,提出了一种自适应的面向数据流的频繁子图挖掘方法,该方法能够通过侦测数据流中的样本分布和数据特征来动态调整子图搜寻策略,有效减少了挖掘时间；研究了面向数据流的频繁子图挖掘(Frequent Sub-graph Mining)中的概念漂移(Concept drift)问题,提出了基于数据流窗口分片和样本库的概念漂移侦测方法。
(4)针对传统个性化信息服务系统在应对用户兴趣变化问题中所采用的适应性技术相对单调和孤立的问题,研究了软件进化方法学,包括设计模式、软件进化、需求工程(Requirement Engineering)、面向方面编程等内容,设计了移动个性化信息服务系统进化框架,包括框架可行性研究和相关模块的搭建,该框架能够将用户兴趣变化映射为系统构件之间关联的变化,并同时使用软件工程中的方法学来指导系统进化过程,达到提高复用(Reuse)率、提高进化质量等目的,有一定参考价值。
While the Web and communication network have become main tools of people for acquiring knowledge, the problem of Information Overload emerged. To solve this problem, personalized Information service system aims at providing users information, which is consistent to their interests. Currently, novel frontier, which is referred as Mobile Computing, has become the trend of Web and brought new challenges for personalized service. The working mechanism and the service model of traditional systems are consisted of two main steps commonly. Firstly, acquire user feedback and analysis users'interest during the interaction. Secondly, customize specific contents by modify the value of existed user model and change parameters of service model. However, the effects of the process can not work so well when used in Mobile Computing. Since mobile service takes several new complex factors into account, this leads to the inefficiency of adaptation for user interest changing. Further more, traditional methods depend on feedback excessively, there are rare research focusing on the rule, which triggers users'interest changes. The lazy learning lags behind the interest changing and then provides reactions too passively.
This paper aims to resolve the problem mentioned above, dedicates to research the active evolution mechanism, including the organization improvements of components, the optimization of appearance of the system. Beside, we should ensure the feasibility of the evolution mechanisms. This paper made efforts on the orientation of user interests change, system evolution with constrains, the methodology of software evolution. The main researches and innovations are as follows:
(1) Research the detailed orientation of the interest changing and implicit rules, proposed methods for acquired interest changing knowledge, which are caused by user cognition, information seeking behavior, and context changing, propose methods to predict user interest and improve the quality of information service.
(2) Research the evolution mechanisms of mobile personalization information system. Proposed the methods of the reorganization of contents on server, the content adaptation in terminals and the constraints of these re-factors. Propose an intelligent method for configuration recommendation on mobile terminals. This method can recomment configurations to user based on the current context.
(3) With the interaction between large numbers of users and contents, large scale data will be generated. Beside, in real cases, the large scale data is in the form of data stream, which comes from distributed places. Thus, research the data mining methods over the fast arriving data streams, which include the frequent item-set mining and frequent sub-graph mining. Proposed a frequent sub-graph mining methods over data streams, which can handle concept drift by keeping a concept samples during mining.
(4) The evolution of software systems, may encounter many problems in software engineering problem, which caused by redevelopment. Therefore, this paper researched design pattern, software evolution, requirement engineering and etc. This paper aimed to combine the user interest changing and software evolution, map the change of user interest changing to the changing of system components, by which we can further guide the development and evolution of mobile personalized information system. Such activities can improve the flexibility and scalability of the system, and have benefits on mobile personalized information system evolution.
本文旨在弥补传统个性化服务机制在移动领域表现出的上述不足,致力于研究移动个性化信息服务系统的进化机制,以实现如下愿景：系统能够自动进化其服务内容、组织结构和界面外观以适应移动用户多变的兴趣。并且,进化机制具备工程实现的可行性。基于此想法,本文研究内容包括四方面：移动用户兴趣变化的驱动因素与规律研究、移动个性化系统进化机制研究、面向数据流的用户兴趣数据挖掘研究、软件进化(Software evolution)方法学研究。本文所做的主要工作和创新点如下：
(1)针对移动个性化信息服务难以有效适应用户需求变化的问题,提出了一种面向行为动机的个性化信息服务模型,通过使用隐马尔科夫模型建模用户的兴趣变化,一定程度上解决了大跨度的用户兴趣变化预测问题,并借此改进了个性化服务的质量；针对无法准确度量用户行为模式兴趣度的问题,通过引入用户信息获取行为(Information Seeking Behavior)规律,提出了一种基于关联规则预期支持度的兴趣度度量方法,提高了关联规则兴趣度的预测准确度；针对难以预测用户移动规律的问题,通过引入空间认知,提出了基于空间认知分析的用户移动规律预测方法,提高了预测准确度。
(2)研究了移动个性化信息服务系统进化机制,提出了一种针对Web页面的布局优化方法；针对需要为不同用户维持不同版本的站点内容而增加服务器负担的问题,提出了一种个性化页面标签转换与内容自动生成机制,能够为用户动态生成个性化的服务内容；针对移动终端计算能力低,无法执行机器学习算法以提取用户兴趣的问题,提出了一种具有智能性质的客户端程序自动配置方法,该方法可以在较短时间内计算用户个性化配置偏好,并在适当上下文环境中进行配置推荐。
(3)研究了用户兴趣数据的挖掘技术,针对需要从快速到达后台系统的用户信息行为数据流中尽快挖掘到用户行为规律的问题,提出了一种自适应的面向数据流的频繁子图挖掘方法,该方法能够通过侦测数据流中的样本分布和数据特征来动态调整子图搜寻策略,有效减少了挖掘时间；研究了面向数据流的频繁子图挖掘(Frequent Sub-graph Mining)中的概念漂移(Concept drift)问题,提出了基于数据流窗口分片和样本库的概念漂移侦测方法。
(4)针对传统个性化信息服务系统在应对用户兴趣变化问题中所采用的适应性技术相对单调和孤立的问题,研究了软件进化方法学,包括设计模式、软件进化、需求工程(Requirement Engineering)、面向方面编程等内容,设计了移动个性化信息服务系统进化框架,包括框架可行性研究和相关模块的搭建,该框架能够将用户兴趣变化映射为系统构件之间关联的变化,并同时使用软件工程中的方法学来指导系统进化过程,达到提高复用(Reuse)率、提高进化质量等目的,有一定参考价值。
While the Web and communication network have become main tools of people for acquiring knowledge, the problem of Information Overload emerged. To solve this problem, personalized Information service system aims at providing users information, which is consistent to their interests. Currently, novel frontier, which is referred as Mobile Computing, has become the trend of Web and brought new challenges for personalized service. The working mechanism and the service model of traditional systems are consisted of two main steps commonly. Firstly, acquire user feedback and analysis users'interest during the interaction. Secondly, customize specific contents by modify the value of existed user model and change parameters of service model. However, the effects of the process can not work so well when used in Mobile Computing. Since mobile service takes several new complex factors into account, this leads to the inefficiency of adaptation for user interest changing. Further more, traditional methods depend on feedback excessively, there are rare research focusing on the rule, which triggers users'interest changes. The lazy learning lags behind the interest changing and then provides reactions too passively.
This paper aims to resolve the problem mentioned above, dedicates to research the active evolution mechanism, including the organization improvements of components, the optimization of appearance of the system. Beside, we should ensure the feasibility of the evolution mechanisms. This paper made efforts on the orientation of user interests change, system evolution with constrains, the methodology of software evolution. The main researches and innovations are as follows:
(1) Research the detailed orientation of the interest changing and implicit rules, proposed methods for acquired interest changing knowledge, which are caused by user cognition, information seeking behavior, and context changing, propose methods to predict user interest and improve the quality of information service.
(2) Research the evolution mechanisms of mobile personalization information system. Proposed the methods of the reorganization of contents on server, the content adaptation in terminals and the constraints of these re-factors. Propose an intelligent method for configuration recommendation on mobile terminals. This method can recomment configurations to user based on the current context.
(3) With the interaction between large numbers of users and contents, large scale data will be generated. Beside, in real cases, the large scale data is in the form of data stream, which comes from distributed places. Thus, research the data mining methods over the fast arriving data streams, which include the frequent item-set mining and frequent sub-graph mining. Proposed a frequent sub-graph mining methods over data streams, which can handle concept drift by keeping a concept samples during mining.
(4) The evolution of software systems, may encounter many problems in software engineering problem, which caused by redevelopment. Therefore, this paper researched design pattern, software evolution, requirement engineering and etc. This paper aimed to combine the user interest changing and software evolution, map the change of user interest changing to the changing of system components, by which we can further guide the development and evolution of mobile personalized information system. Such activities can improve the flexibility and scalability of the system, and have benefits on mobile personalized information system evolution.
引文
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