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.

翻译：点击率（CTR）预测是推荐系统中最重要的机器学习任务之一，为数以亿计的用户提供个性化体验。神经架构搜索（NAS）作为一个新兴领域，已展现出其在发现强大神经网络架构方面的能力，这促使我们探索其在CTR预测中的应用潜力。由于存在1）多样化的非结构化特征交互，2）异构特征空间，以及3）高数据量与内在数据随机性，为推荐模型有效构建、搜索和比较不同架构具有挑战性。为解决这些挑战，我们提出了一种名为AutoCTR的自动化交互架构发现框架用于CTR预测。通过将简单但具有代表性的交互模块化为虚拟构建块，并将其连接成有向无环图空间，AutoCTR在架构层面采用基于学习排序指导的进化架构探索，并利用低保真度模型实现加速。实证分析表明，与人工设计的架构相比，AutoCTR在不同数据集上均表现出有效性。所发现的架构在不同数据集间还具备良好的泛化性与可迁移性。