Click-Through Rate (CTR) prediction is one of the most important machine
learning tasks in recommender systems, driving personalized experience for
billions of consumers. Neural architecture search (NAS), as an emerging field,
has demonstrated its capabilities in discovering powerful neural network
architectures, which motivates us to explore its potential for CTR predictions.
Due to 1) diverse unstructured feature interactions, 2) heterogeneous feature
space, and 3) high data volume and intrinsic data randomness, it is challenging
to construct, search, and compare different architectures effectively for
recommendation models. To address these challenges, we propose an automated
interaction architecture discovering framework for CTR prediction named
AutoCTR. Via modularizing simple yet representative interactions as virtual
building blocks and wiring them into a space of direct acyclic graphs, AutoCTR
performs evolutionary architecture exploration with learning-to-rank guidance
at the architecture level and achieves acceleration using low-fidelity model.
Empirical analysis demonstrates the effectiveness of AutoCTR on different
datasets comparing to human-crafted architectures. The discovered architecture
also enjoys generalizability and transferability among different datasets.

本文提出了一种基于自动交互架构发现的 CTR 预测框架 AutoCTR，使用神经架构搜索技术探索了不同数据集的 CTR 预测模型，取得了在不同数据集上的预测效果和泛化性能的成功。