A mobile recommendation system based on Logistic Regression and Gradient Boosting Decision Trees

被引：0

作者：

Wang, Yaozheng ^{[1
]}

Feng, Dawei ^{[1
]}

Ii, Dongsheng ^{[1
]}

Chen, Xinyuan ^{[1
]}

Zhac, Yunxiang ^{[1
]}

Niu, Xin ^{[1
]}

机构：

[1] Natl Univ Def Technol, Sch Comp Sci, Natl Lab Parallel & Distributed Proc, Changsha 410003, Hunan, Peoples R China

来源：

2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2016年

基金：

中国国家自然科学基金;

关键词：

NETWORKS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Real-life behaviors shown by the mobile users typically exhibit plenty noises, making it hard to construct an effective recommendation engine. In this paper, we present a fused model based on the LR algorithm and the GBDT algorithm to recommend vertical industry commodities in a mobile setting. A set of specifically designed methods are proposed to deal with the data preprocessing and feature extraction problem for the mobile recommendation scenario. The proposed method is evaluated on a large scale real-world dataset provided by the Alibaba mobile shopping department. Result on the F1 score has seen an improvement of 2%-36% compared with the baseline.

引用

页码：1896 / 1902

页数：7

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