De-biased dart ensemble model for personalized recommendation

Personalized recommendation aims to use the historical behavior of users to recommend new items that are likely to be of interest to them. Due to a tiny improvement of it can lead to a huge profits, lots of giant e-commerce companies, such as Amazon, Alibaba and eBay, have put their great effort on this field. In this paper, we formulate the problem of personalized recommendation as a tree based regression task and present a de-biased DART ensemble model. To generate compact personalized profile of user, a decision tree based sparse encoding approach is first proposed to refine those raw behaviors, like clicks, views, and purchases from user-item interaction. Considering the problem of class imbalance in training the predictive models, resampling based de-biased DART model is utilized to form multiple weak predictors for recommendation. Thus, based on these learnt weak predictors, a stronger predictor can be built. Experimental results on real commercial dataset demonstrate the effectiveness of the proposed personalized recommendation model.