# Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction

arXiv: https://arxiv.org/abs/2012.06968 · HTML: https://mlsanity.com/paper/2012.06968/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection](https://mlsanity.com/paper/2505.19435/) (2025)
- [Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2202.08959/) (2022)
- [Triangle Graph Interest Network for Click-through Rate Prediction](https://mlsanity.com/paper/2202.02698/) (2022)
- [AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking](https://mlsanity.com/paper/2310.09706/) (2023)
- [TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems](https://mlsanity.com/paper/2507.03895/) (2025)
- [FINT: Field-aware INTeraction Neural Network For CTR Prediction](https://mlsanity.com/paper/2107.01999/) (2021)
- [SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation](https://mlsanity.com/paper/2108.08022/) (2021)
- [AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System](https://mlsanity.com/paper/2501.15816/) (2025)
- [Fine-Grained Zero-Shot Composed Image Retrieval with Complementary Visual-Semantic Integration](https://mlsanity.com/paper/2601.14060/) (2026)
- [FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems](https://mlsanity.com/paper/2210.07768/) (2022)
- [PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction](https://mlsanity.com/paper/2403.10049/) (2024)
- [From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals](https://mlsanity.com/paper/2508.10851/) (2025)
- [FairFS: Addressing Deep Feature Selection Biases for Recommender System](https://mlsanity.com/paper/2602.20001/) (2026)
- [Can LLMs Fool Graph Learning? Exploring Universal Adversarial Attacks on Text-Attributed Graphs](https://mlsanity.com/paper/2603.21155/) (2026)

## Abstract (arXiv)

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. First, existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction, because users often click on new products that are irrelevant to any historical behaviors. Second, in the real scenario, there exist numerous users that have operations a long time ago, but turn relatively inactive in recent times. Thus, it is hard to precisely capture user's current preferences through early behaviors. Third, multiple representations of user's historical behaviors in different feature subspaces are largely ignored. To remedy these issues, we propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features (e.g., gender, age and occupation in user-profile). Specifically, MIAN contains a Multi-Interactive Layer (MIL) that integrates three local interaction modules to capture multiple representations of user preference through sequential behaviors and simultaneously utilize the fine-grained user-specific as well as context information. In addition, we design a Global Interaction Module (GIM) to learn the high-order interactions and balance the different impacts of multiple features. Finally, Offline experiment results from three datasets, together with an Online A/B test in a large-scale recommendation system, demonstrate the effectiveness of our proposed approach.