Click-Through Rate Prediction Model Based on Neural Architecture Search

Click-through rate(CTR) prediction is an important task in the recommendation system.Its goal is to predict the pro-bability of a user clicking on an advertisement or item.Feature embedding and feature interacting are critical for prediction performance.Therefore,the ideas of many click-through rate prediction models are optimized based on these two aspects.However,most of the work only focus on one of the aspects,and almost all models do not distinguish features in feature interacting.The same embedding and interacting method are used when crossing the same feature with other features,which hinders the improvement of model performance.In order to solve this problem,the automatic super-field-aware feature embedding and interacting(Auto-SEI) model is proposed.Firstly,it assigns each sub-field to a super-field,and obtains the feature embedding according to the grouping,then selects appropriate interacting method for the feature pair to obtain the cross feature,and finally makes prediction.In Auto-SEI model,the division of sub-field and the selection of interacting methods are parameterized as an architecture search problem,and the neural architecture search(NAS) algorithm is used to compress the search space and make selections.A large number of experiments are conducted on three real large-scale data sets and the results show the excellent performance of the Auto-SEI model on the task of click-through rate prediction.