Research Area:  Machine Learning

Abstract:

Click-Through Rate (CTR) prediction has been widely used in many machine learning tasks such as online advertising and personalization recommendation. Unfortunately, given a domain-specific dataset, searching effective feature interaction operations and combinations from a huge candidate space requires significant expert experience and computational costs. Recently, Neural Architecture Search (NAS) has achieved great success in discovering high-quality network architectures automatically. However, due to the diversity of feature interaction operations and combinations, the existing NAS-based work that treats the architecture search as a black-box optimization problem over a discrete search space suffers from low efficiency. Therefore, it is essential to explore a more efficient architecture search method. To achieve this goal, we propose NAS-CTR, a differentiable neural architecture search approach for CTR prediction. First, we design a novel and expressive architecture search space and a continuous relaxation scheme to make the search space differentiable. Second, we formulate the architecture search for CTR prediction as a joint optimization problem with discrete constraints on architectures and leverage proximal iteration to solve the constrained optimization problem. Additionally, a straightforward yet effective method is proposed to eliminate the aggregation of skip connections. Extensive experimental results reveal that NAS-CTR can outperform the SOTA human-crafted architectures and other NAS-based methods in both test accuracy and search efficiency.

Keywords:  
Click-Through Rate
Neural Architecture Search
Black-box optimization
CTR prediction
NAS-CTR

Author(s) Name:  Guanghui Zhu, Feng Cheng, Defu Lian, Chunfeng Yuan, Yihua Huang

Publisher name:  ACM Library

DOI:  10.1145/3477495.3532030

Volume Information:  

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3477495.3532030