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

Abstract

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.