# MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/2008.02974 · HTML: https://mlsanity.com/paper/2008.02974/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

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

- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction](https://mlsanity.com/paper/2106.02768/) (2021)
- [Image Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2502.15694/) (2024)
- [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](https://mlsanity.com/paper/2105.08909/) (2021)
- [LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2506.17966/) (2025)
- [Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation](https://mlsanity.com/paper/2208.11457/) (2022)
- [A Collaborative Transfer Learning Framework for Cross-domain Recommendation](https://mlsanity.com/paper/2306.16425/) (2023)
- [Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach](https://mlsanity.com/paper/2311.04760/) (2023)
- [Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks](https://mlsanity.com/paper/2204.00888/) (2022)
- [Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model](https://mlsanity.com/paper/2403.17425/) (2024)
- [Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation](https://mlsanity.com/paper/2302.05990/) (2023)
- [Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation](https://mlsanity.com/paper/2302.14438/) (2023)
- [ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation](https://mlsanity.com/paper/2407.02542/) (2024)
- [Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2504.15085/) (2025)
- [MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed](https://mlsanity.com/paper/2304.09087/) (2023)
- [DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems](https://mlsanity.com/paper/2305.12058/) (2023)
- [Effective Two-Stage Knowledge Transfer for Multi-Entity Cross-Domain Recommendation](https://mlsanity.com/paper/2402.19101/) (2024)
- [Enhancing CTR Prediction in Recommendation Domain with Search Query Representation](https://mlsanity.com/paper/2410.21487/) (2024)
- [Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao](https://mlsanity.com/paper/2411.13057/) (2024)
- [Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation](https://mlsanity.com/paper/2504.07102/) (2025)
- [GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model](https://mlsanity.com/paper/2402.13667/) (2024)
- [FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction](https://mlsanity.com/paper/2407.18472/) (2024)
- [EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation](https://mlsanity.com/paper/2407.20121/) (2024)
- [DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation](https://mlsanity.com/paper/2410.10835/) (2024)
- [A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning](https://mlsanity.com/paper/2411.09286/) (2024)
- [Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling](https://mlsanity.com/paper/2502.16239/) (2025)
- [Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation](https://mlsanity.com/paper/2503.16875/) (2025)
- [GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation](https://mlsanity.com/paper/2507.05142/) (2025)
- [Cross-attention Secretly Performs Orthogonal Alignment in Recommendation Models](https://mlsanity.com/paper/2510.09435/) (2025)
- [Causality Enhancement for Cross-Domain Recommendation](https://mlsanity.com/paper/2510.14641/) (2025)

## Abstract (arXiv)

Click-through rate (CTR) prediction is a critical task in online advertising systems. Existing works mainly address the single-domain CTR prediction problem and model aspects such as feature interaction, user behavior history and contextual information. Nevertheless, ads are usually displayed with natural content, which offers an opportunity for cross-domain CTR prediction. In this paper, we address this problem and leverage auxiliary data from a source domain to improve the CTR prediction performance of a target domain. Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad. In order to effectively leverage news data for predicting CTRs of ads, we propose the Mixed Interest Network (MiNet) which jointly models three types of user interest: 1) long-term interest across domains, 2) short-term interest from the source domain and 3) short-term interest in the target domain. MiNet contains two levels of attentions, where the item-level attention can adaptively distill useful information from clicked news / ads and the interest-level attention can adaptively fuse different interest representations. Offline experiments show that MiNet outperforms several state-of-the-art methods for CTR prediction. We have deployed MiNet in UC Toutiao and the A/B test results show that the online CTR is also improved substantially. MiNet now serves the main ad traffic in UC Toutiao.