因子分解机算法在基于深度数据包检测的手机应用推荐中的应用

doi:10.11772/j.issn.1001-9081.2016.02.0307

计算机应用 ›› 2016, Vol. 36 ›› Issue (2): 307-310.DOI: 10.11772/j.issn.1001-9081.2016.02.0307

• 第三届CCF大数据学术会议(CCF BigData 2015) • 上一篇下一篇

因子分解机算法在基于深度数据包检测的手机应用推荐中的应用

孙良君^1,2, 范剑锋¹, 杨婉琪², 史颖欢²

1. 中博信息技术研究院有限公司, 南京 210012;
2. 计算机软件新技术国家重点实验室(南京大学), 南京 210046

收稿日期:2015-08-29 修回日期:2015-09-19 出版日期:2016-02-10 发布日期:2016-02-03
通讯作者: 范剑锋(1989-),男,江苏盐城人,硕士研究生,主要研究方向:数据挖掘、机器学习
作者简介:孙良君(1978-),男,安徽安庆人,高级工程师,硕士,主要研究方向:大数据、云计算;杨婉琪(1988-),女,江苏徐州人,博士研究生,主要研究方向:数字图像处理、机器学习;史颖欢(1985-),男,江苏无锡人,讲师,博士,主要研究方向:医学图像处理、机器学习。

Application of factorization machine in mobile App recommendation based on deep packet inspection

SUN Liangjun^1,2, FAN Jianfeng¹, YANG Wanqi², SHI Yinhuan²

1. ZhongBo Information Technology Research Institute Company Limited, Nanjing Jiangsu 210012, China;
2. State Key Laboratory for Novel Software Technology(Nanjing University), Nanjing Jiangsu 210046, China

Received:2015-08-29 Revised:2015-09-19 Online:2016-02-10 Published:2016-02-03

摘要/Abstract

摘要： 为了从网络数据包中抽取相关特征进行手机应用推荐,使用江苏电信运营商在互联网服务提供商(ISP)机房抽取的网络深度数据包数据,从中抽取运营商所关心的热点手机用户的App访问信息,然后使用基于矩阵分解(包括奇异值分解(SVD)和非负矩阵分解(NMF))的推荐算法、奇异值分解推荐算法以及因子分解机推荐算法进行手机App推荐。实验表明,因子分解机算法取得了较好的推荐效果。这说明因子分解机在手机应用推荐的场景中可以更好地描述用户和物品之间的隐含关联。

关键词: 手机App, 深度数据包检测, 奇异值分解, 非负矩阵分解, 因子分解机

Abstract: To extract features from Deep Packet Inspection (DPI) data and perform mobile application recommendation, using the DPI data collected from Internet Service Provider (ISP) in Jiangsu Telecom, the access history data of active users defined by the communications operator was processed by matrix factorization recommendation (including Singular Value Decomposition (SVD) and Non-negtive Matrix Factorization (NMF)), SVD recommendation and factorization machine recommendation algorithms for mobile phone application recommendation. The results show that factorization machine algorithm achieves better performance, it means that factorization machine algorithm can better describe the latent connection in the user-item relationship.

Key words: mobile application, Deep Packet Inspection(DPI), Singular Value Decomposition(SVD), Non-negtive Matrix Factorization(NMF), factorization machine

中图分类号:

TP391

孙良君, 范剑锋, 杨婉琪, 史颖欢. 因子分解机算法在基于深度数据包检测的手机应用推荐中的应用[J]. 计算机应用, 2016, 36(2): 307-310.

SUN Liangjun, FAN Jianfeng, YANG Wanqi, SHI Yinhuan. Application of factorization machine in mobile App recommendation based on deep packet inspection[J]. Journal of Computer Applications, 2016, 36(2): 307-310.

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因子分解机算法在基于深度数据包检测的手机应用推荐中的应用

Application of factorization machine in mobile App recommendation based on deep packet inspection

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