# Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/2007.06434 · HTML: https://mlsanity.com/paper/2007.06434/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 30 of 37)

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- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
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- [Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2202.08959/) (2022)
- [AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems](https://mlsanity.com/paper/2606.26859/) (2026)
- [NASRec: Weight Sharing Neural Architecture Search for Recommender Systems](https://mlsanity.com/paper/2207.07187/) (2022)
- [Differentiable NAS Framework and Application to Ads CTR Prediction](https://mlsanity.com/paper/2110.14812/) (2021)
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- [ColdNAS: Search to Modulate for User Cold-Start Recommendation](https://mlsanity.com/paper/2306.03387/) (2023)
- [General Information Metrics for Improving AI Model Training Efficiency](https://mlsanity.com/paper/2501.02004/) (2025)
- [Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale](https://mlsanity.com/paper/2311.08430/) (2023)
- [Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models](https://mlsanity.com/paper/2411.15731/) (2024)
- [DistDNAS: Search Efficient Feature Interactions within 2 Hours](https://mlsanity.com/paper/2311.00231/) (2023)
- [Random Intersection Chains](https://mlsanity.com/paper/2104.04714/) (2021)
- [Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation](https://mlsanity.com/paper/2211.11159/) (2022)
- [DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification](https://mlsanity.com/paper/2307.06005/) (2023)
- [SiGeo: Sub-One-Shot NAS via Information Theory and Geometry of Loss Landscape](https://mlsanity.com/paper/2311.13169/) (2023)
- [CADC: Encoding User-Item Interactions for Compressing Recommendation Model Training Data](https://mlsanity.com/paper/2407.08108/) (2024)

## Abstract (arXiv)

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.