一种上下文移动用户偏好自适应学习方法

摘要

A mobile network has higher demands for the performance of personalized mobile network services, but existing researches have been unable to modify the contextual mobile user preferences adaptively and provide real-time, accurate personalized mobile network services for mobile users. This paper proposes a context computing-based approach to mobile user preferences adaptive learning, which can ensure the accuracy and the response time. First, through analyzing the logs of contextual mobile user behaviors, the method judges whether mobile user behaviors are affected by context or not, and detects whether the contextual mobile user behaviors change. According to these judgments, the contextual mobile user preferences are modified. Secondly, the context is introduced into the least squares support vector machine (LSSVM), which is employed to learn the changed contextual mobile user preferences. Further, a learning method of contextual mobile user preferences is proposed which is based on context of the least squares support vector machine (C-LSSVM). Finally, the experimental results show that the proposed method is superior to other learning methods when considering both accuracy and response time. The proposed method in this paper can be applied in the system of personalized mobile network services.%针对移动网络对个性化移动网络服务系统的性能提出了更高的要求,但现有研究难以自适应地修改上下文移动用户偏好以为移动用户提供实时、准确的个性化移动网络服务的问题,提出了一种上下文移动用户偏好自适应学习方法,在保证精确度的基础上缩短了学习的响应时间.首先,通过分析移动用户行为日志来判断移动用户行为是否受上下文影响,并在此基础上判断移动用户行为是否发生变化.然后,根据判断结果对上下文移动用户偏好进行修正.在对发生变化的上下文移动用户偏好进行学习时,将上下文引入到最小二乘支持向量机中,进一步提出了基于上下文最小二乘支持向量机(C-LSSVM)的上下文移动用户偏好学习方法.最后,实验结果表明,当综合考虑精确度和响应时间两方面因素时,所提出的方法优于其他学习方法,并且可应用于个性化移动网络服务系统中.