移动互联环境下基于改进Markov融合模型的用户行为预测算法

User behavior prediction algorithm based on improved Markov fusion model in mobile Internet environment

在移动互联环境下,随着用户需求的不断增加,业务类型变得日益多样。然而不同类型的网络业务具有完全不同的Qo S指标要求和网络资源需求,从而导致有限的网络资源难以同时满足各类业务的Qo S要求。通过对各用户下一业务类型的预测,相应地预留并配置最佳的网络资源,可有效解决上述问题。因此,提出一种基于改进Markov融合模型的用户行为预测算法。首先,建立面向各单一用户的多阶Markov预测模型,进而引入业务偏爱度修正上述模型,以提高行为预测的准确度;其次,利用用户综合相似度,建立目标用户的最近邻用户集,以此形成多用户多阶Markov融合预测模型,从而实现对目标用户行为的精准预测。仿真结果验证了该算法的有效性。

In mobile Internet environment,with the increase of user demands,service types become more and more diverse.However,different types of network services have completely different requirements of QoS metrics and network resources.Hence,the limited network resources cannot satisfy the QoS requirements of various services at once.By predicting each user’s next service type,the optimal network resources can be reserved and allocated accordingly,so as to solve the above problem effectively.Therefore,this paper proposed an improved Markov fusion model based user behavior prediction algorithm.First,it established a multi-order Markov prediction model for a single user,and then introduced service preference degree to improve the prediction accuracy of the above model.Second,based on the integrated similarity metric,it obtained the nearest neighbor set of a target user,and then formulated a multi-user and multi-order Markov fusion prediction model,so as to achieve the accurate predictions of target user’s behaviors.Simulation results verify the effectiveness of proposed algorithm.