CLICK-THROUGH RATE PREDICTION MODEL BASED ON HOFIBIAFM
In the recommender system,deep learning models such as FiBiNET and AFM can focus on the importance of features for click-through rate prediction.FiBiNET's deep model uses DNN network to model feature interaction quite implicitly,but using DNN to learn higher-order features may lead to dilution of lower-order feature crossing.High-order important features were learned by superposing multiple SENET attention mechanisms and high-order attentive factorization machine was added to update feature representations.A new click rate prediction model HoFiBiAFM was formed.By comparing the classification task and regression task with other CTR prediction models on the Movielens-100K and Movielens-IM datasets,HoFiBiAFM's click-through prediction performance was verified.
万方数据
维普
