Abstract：With the development of deep learning，in recent years，the research of CTR prediction model is often based on deep learning and uses different feature intersection methods to achieve the performance improvement of CTR prediction model.The latest and most effective research result is xDeepFM，which integrates sub-models for implicit and explicit high-order feature intersection methods.However，our experiments have found that the sub-model selection of xDeepFM is not perfect，and the combination strategy of sub-models is too simple.In this regard，we propose a new model that not only improves the selection of sub-models，but also uses attention mechanism to improve the combination of sub-models.For convenience，the new model we propose is called Attentional-xDeepFM-C in this article.We conducted experiments on the Avazu and Criteo datasets.The AUC scores of the new model under the two datasets were 2.17% and 4.97% higher than the xDeepFM model，respectively.We have released the source code of the Attentional-xDeepFM-C model on open site.
王越,于莲芝. 一个以注意力机制结合隐式和显式的特征交叉的CTR预估模型[J]. 小型微型计算机系统, 2021, 42(9): 1884-1890.
WANG Yue,YU Lian-zhi. CTR Prediction Model Combining Implicit and Explicit Features with Attention Mechanism. Journal of Chinese Computer Systems, 2021, 42(9): 1884-1890.
2021, Vol. 42 
