QoS-aware temporal prediction model for personalized service recommendation

被引：0

作者：

Peng, Fei ^{[1
,2
]}

Deng, Haojiang ^{[2
]}

Liu, Lei ^{[2
]}

机构：

[1] University of Chinese Academy of Sciences

[2] National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2013年 / 40卷 / 04期

关键词：

Accuracy; Baseline model; Matrix factorization model; Quality of service; Service recommendation;

D O I：

10.3969/j.issn.1001-2400.2013.04.029

中图分类号：

学科分类号：

摘要：

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.

引用

页码：174 / 180

页数：6

共 12 条

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