A mobile recommendation system based on logistic regression and Gradient Boosting Decision Trees

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 se...

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Bibliographic Details
Published in2016 International Joint Conference on Neural Networks (IJCNN) pp. 1896 - 1902
Main Authors Yaozheng Wang, Dawei Feng, Dongsheng Li, Xinyuan Chen, Yunxiang Zhao, Xin Niu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2016
Subjects
Computational modeling
Data models
Feature extraction
Logistics
Mobile communication
Prediction algorithms
Predictive models
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Summary: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.
ISSN:2161-4407
DOI:10.1109/IJCNN.2016.7727431