Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

Effectively modeling feature interactions is crucial for CTR prediction in industrial recommender systems. The state-of-the-art deep CTR models with parallel structure (e.g., DCN) learn explicit and implicit feature interactions through independent parallel networks. However, these models suffer from trivial sharing issues, namely insufficient sharing in hidden layers and excessive sharing in network input, limiting the model's expressiveness and effectiveness. Therefore, to enhance information sharing between explicit and implicit feature interactions, we propose a novel deep CTR model EDCN. EDCN introduces two advanced modules, namely bridge module and regulation module, which work collaboratively to capture the layer-wise interactive signals and learn discriminative feature distributions for each hidden layer of the parallel networks. Furthermore, two modules are lightweight and model-agnostic, which can be generalized well to mainstream parallel deep CTR models. Extensive experiments and studies are conducted to demonstrate the effectiveness of EDCN on two public datasets and one industrial dataset. Moreover, the compatibility of two modules over various parallel-structured models is verified, and they have been deployed onto the online advertising platform in Huawei, where a one-month A/B test demonstrates the improvement over the base parallel-structured model by 7.30% and 4.85% in terms of CTR and eCPM, respectively.