[KDD 2020] Towards Automated Neural Interaction Discovering for Click-Through Rate Prediction
Aug 13, 20203 views
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.