CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction

Abstract

The study of feature interactions in deep neural network-based recommender systems has been a popular research area in industry and academic circles. However, the vast majority of parallel CTR prediction models do not classify the input features but instead feed them into the model. This way not only reduces the accuracy of the model but also ignores the effectiveness of learning individual feature interactions. In addition, the majority of parallel CTR prediction models only focus on the submodel intersections of their parallel models, ignoring the importance of the external intersection. To address the shortcomings, this paper proposes the CCPIN model on the basis of the XdeepFM model. In the CCPIN model, it can not only learn different category feature interactions but also learn individual feature interactions. Through the classification gate, adaptive features are maximized to improve the performance of the submodel. Through the Combine layer, the interaction of submodel results can be learned while retaining the original output. Through comparison experiments with other models on two datasets, it is demonstrated that the CCPIN model has an average increase of 0.93% in AUC and a decrease of 0.47% in Logloss compared to other models.