Architecture and Operation Adaptive Network for Online Recommendations

Learning feature interactions is crucial for model performance in online recommendations. Extensive studies are devoted to designing effective structures for learning interactive information in an explicit way and tangible progress has been made. However, the core interaction calculations of these models are artificially specified, such as inner product, outer product and self-attention, which results in high dependence on domain knowledge. Hence model effect is bounded by both restriction of human experience and the finiteness of candidate operations. In this paper, we propose a generalized interaction paradigm to lift the limitation, where operations adopted by existing models can be regarded as its special form. Based on this paradigm, we design a novel model to adaptively explore and optimize the operation itself according to data, named generalized interaction network(GIN). We proved that GIN is a generalized form of a wide range of state-of-the-art models, which means GIN can automatically search for the best operation among these models as well as a broader underlying architecture space. Finally, an architecture adaptation method is introduced to further boost the performance of GIN by discriminating important interactions. Thereby, architecture and operation adaptive network(AOANet) is presented. Experiment results on two large scale datasets show the superiority of our model. AOANet has been deployed to industrial production. In a 7-day A/B test, the click-through rate increased by 10.94%, which represents considerable business benefits.