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.

中文翻译：

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面向点击率预测的自动神经交互发现

点击率 (CTR) 预测是推荐系统中最重要的机器学习任务之一，为数十亿消费者提供个性化体验。神经架构搜索（NAS）作为一个新兴领域，已经展示了其发现强大神经网络架构的能力，这促使我们探索其在 CTR 预测方面的潜力。由于 1) 多样化的非结构化特征交互，2) 异构特征空间，以及 3) 高数据量和内在数据随机性，为推荐模型有效地构建、搜索和比较不同的体系结构具有挑战性。为了应对这些挑战，我们提出了一种名为 AutoCTR 的 CTR 预测自动化交互架构发现框架。通过将简单但具有代表性的交互模块化为虚拟构建块，并将它们连接到有向无环图的空间中，AutoCTR 在架构级别通过学习排名指导执行进化架构探索，并使用低保真模型实现加速。与人造架构相比，实证分析证明了 AutoCTR 在不同数据集上的有效性。发现的架构还享有不同数据集之间的通用性和可转移性。与人造架构相比，实证分析证明了 AutoCTR 在不同数据集上的有效性。发现的架构还享有不同数据集之间的通用性和可转移性。与人造架构相比，实证分析证明了 AutoCTR 在不同数据集上的有效性。发现的架构还享有不同数据集之间的通用性和可转移性。