Abstract: Click-through rate prediction plays a crucial role in recommender systems and online advertisements,and feature embedding and feature interactions are key factors affecting prediction accuracy.However,many existing models mainly focus on designing feature interaction structures,and they usually use simple computational methods such as Hadamard product,inner pro-duct,single vector-level or bit-level feature interaction or combining multilayer perceptron for implicit feature interaction,which may have limitations in dealing with complex feature interactions.Tomake up for the above shortcomings,a click-through rate prediction model based on feature embedding gating and polynomial feature crossover networks is proposed.First,in order to achieve more effective feature interactions,polynomial featurecrossover network is proposed,where the network realizes feature crossover by combining Hadamard product and inner product to achieve explicit higher-order feature crossover in a recursive form.Then,fine-grained feature interaction is achieved by fusing two parallel polynomial feature crossover networks for vector-level and bit-level feature crossover.Finally,in order to dynamically learn the importance of feature embeddings and increase the variability of the inputs to the feature interaction network,feature embedding gating is proposed,which learns the weights of the features from the vector level and the bit level so that the interaction network can be more targeted to capture different feature interaction information.The model performance is evaluated on four open benchmark datasets,and the model achieves AUC and Logloss of 0.814 9 and 0.437 2 respectively,on the Criteo dataset;0.766 3 and 0.366 1 on the Avazu dataset;0.971 6 and 0.198 4 on the Movielens dataset;and 0.985 8 and 0.138 7 on the Frappe dataset.The experimental results show that the proposedmodel exhibits better performance in click-through-rate prediction,and effectively improves the prediction accuracy.

Key words: Recommendation system, Click-through rate prediction, Feature interaction, Feature embedding gating, Fine-grained interaction