TMRM: Two-Stage Multi-Task Recommendation Model Boosted Feature Selection
Fan Zhu, Juan Yang, Pengfei Wang · 2019
For recommendation systems, full learning from features is critical to improving system accuracy. Most recommendation systems collect a large number of features to improve the recommendation accuracy, but they ignore the importance of extracting important feature combinations. Even if some systems manually measure important features based on experience, they cannot automatically select important feature combinations, that is why they can not be used to guide model training. In this paper, a Two-stage Multi-task Recommendation Model(TMRM) is proposed which aims to automatically select important feature combinations from massive features, and it also contributes to achieving better recommendations through a combination of tree-based model and neural network. Extensive experiments on two large public data sets are conducted on TMRM, and the results demonstrate the superiority of our proposed method over state-of-the-art solutions on performance of recommendation systems.