Abstract: Feature interaction is crucial in the field of advertising click-through rate(CTR) prediction in recommendation systems.However,current industry practices for feature interaction often rely on matrix transformations such as inner and outer products,which do not introduce additional information and can only serve as a means of measuring the similarity between two vectors.Therefore,such methods may not reliably represent feature interaction and may not effectively improve the performance of CTR prediction.To address this issue,this paper first introduces additional parameters to learn a mapping from the perspective of improving the feature interaction,assuming that this mapping can map the representation of two vectors to the representation of interaction.The process of learning mapping can be achieved through meta-learning,which constructs a learner to represent feature interactions in a functional manner.Additionally,different features may not adopt the same interaction method,and it is impossible to obtain all feature pairs through a single interaction method.Therefore,a set of meta-lear-ners is designed to learn the mapping function,and a GateNet is introduced to learn the distribution of meta-learners in the model,so that a set of meta-learners can represent different feature embeddings.Based on these two points,a feature interaction algorithm is proposed that combines multiple meta-learners with GateNet(gate-MML),which improves the quality of each feature interaction by learning the connections and differences between different features.To verify the performance of the proposed algorithm,gate-MML is used for further feature interaction in the xDeepFM model,and experiments are conducted on two real advertising click-through rate prediction datasets using Logloss as the loss function and AUC as the evaluation metric.Experimental results show that compared with traditional CTR prediction models,the improved algorithm enhances the prediction performance of advertising click-through rate prediction tasks.

Key words: Feature interaction, Advertising click-through rate prediction, Meta-learning, GateNet, Recommender system