J4 ›› 2013, Vol. 40 ›› Issue (4): 174-180.doi: 10.3969/j.issn.1001-2400.2013.04.029

• 研究论文 • 上一篇    下一篇

面向个性化服务推荐的QoS动态预测模型

彭飞1,2;邓浩江2;刘磊2   

  1. (1. 中国科学院大学,北京  100049;
    2. 中国科学院 声学研究所 国家网络新媒体工程技术研究中心,北京  100190)
  • 收稿日期:2012-04-23 出版日期:2013-08-20 发布日期:2013-10-10
  • 通讯作者: 彭飞
  • 作者简介:彭飞(1988-),男,中国科学院声学研究所博士研究生,E-mail: pengf@dsp.ac.cn.
  • 基金资助:

    国家863计划资助项目(2011AA01A102);中国科学院战略性先导科技专项资助项目(XDA06010302)

QoS-aware temporal prediction model for personalized service recommendation

PENG Fei1,2;DENG Haojiang2;LIU Lei2   

  1. (1. University of Chinese Academy of Sciences, Beijing  100049, China;
    2. National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, Beijing  100190, China)
  • Received:2012-04-23 Online:2013-08-20 Published:2013-10-10
  • Contact: PENG Fei

摘要:

针对个性化服务推荐领域现有服务质量预测技术精准度不足的问题,提出一种优化的服务质量动态预测模型.通过引入基线模型,将服务质量预测问题由整体值预测转化为偏差值预测,并结合矩阵分解技术建立了基线矩阵分解模型.通过对客户端和服务端的时间效应进行分析,设计了表示时间效应的矩阵分解模型,与基线矩阵分解模型结合形成了动态基线矩阵分解模型.实验结果表明,与现有服务质量动态预测模型相比,基线矩阵分解模型大幅度提升了服务质量预测精准度,而动态基线矩阵分解模型又在此基础上有进一步的提高.

关键词: 服务推荐, 服务质量, 精准度, 基线模型, 矩阵分解模型

Abstract:

An optimized temporal prediction model for quality of service (QoS) is proposed to improve the prediction accuracy of personalized service recommendation. A baseline model is proposed to transform the prediction task from overall value prediction to bias value prediction, and combined with the matrix factorization technique to build the baseline matrix factorization (BMF) model. Matrix factorization models are designed to denote the time effect of both client and server sides, and then integrated with the BMF model to build the temporal baseline matrix factorization (TBMF) model. Experimental results show that, compared with the existing temporal prediction model for QoS, the BMF model can improve the precision substantially, and that the TBMF model can be improved further.

Key words: service recommendation, quality of service, accuracy, baseline model, matrix factorization model

中图分类号: 

  • TP393