  Personalized search has been a hot research topic for many years and has been
widely used in e-commerce. This paper describes our solution to tackle the
challenge of personalized e-commerce search at CIKM Cup 2016. The goal of this
competition is to predict search relevance and re-rank the result items in SERP
according to the personalized search, browsing and purchasing preferences.
Based on a detailed analysis of the provided data, we extract three different
types of features, i.e., statistic features, query-item features and session
features. Different models are used on these features, including logistic
regression, gradient boosted decision trees, rank svm and a novel deep match
model. With the blending of multiple models, a stacking ensemble model is built
to integrate the output of individual models and produce a more accurate
prediction result. Based on these efforts, our solution won the champion of the
competition on all the evaluation metrics.
