Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction

Traditional Click-Through Rate (CTR) prediction models are usually trained and deployed in a single scenario. However, large-scale commercial platforms usually contain multiple recommendation scenarios, the traffic characteristics of which may be significantly different. Recent studies have proved that learning a unified model to serve multiple scenarios is effective in improving the overall performance. However, most existing approaches suffer from various limitations respectively, such as insufficient distinction modeling, inefficiency with the increase of scenarios, and lack of interpretability. More importantly, as far as we know, none of existing Multi-Scenario Modeling approaches takes explicit feature interaction into consideration when modeling scenario distinctions, which limits the expressive power of the network and thus impairs the performance. In this paper, we propose a novel Scenario-Adaptive Feature Interaction framework named SATrans, which models scenario discrepancy as the distinction of patterns in feature correlations. Specifically, SATrans is built on a Transformer architecture to learn high-order feature interaction and involves the scenario information in the modeling of self-attention to capture distribution shifts across scenarios. We provide various implementations of our framework to boost the performance, and experiments on both public and industrial datasets show that SATrans 1) significantly outperforms existing state-of-the-art approaches for prediction, 2) is parameter-efficient as the space complexity grows marginally with the increase of scenarios, 3) offers good interpretability in both instance-level and scenario-level. We have deployed the model in WeChat Official Account Platform and have seen more than 2.84% online CTR increase on average in three major scenarios. © 2023 ACM.