A mobile recommendation system based on logistic regression and gradient boosting decision trees
Y Wang, D Feng, D Li, X Chen… - 2016 international joint …, 2016 - ieeexplore.ieee.org
2016 international joint conference on neural networks (IJCNN), 2016•ieeexplore.ieee.org
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 …
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 …
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.
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