I am currently a second-year Ph.D. student in the School of Computer Science and Technology at Anhui University, under the supervision of Professor Yiwen Zhang. I have been invited to serve as a reviewer for WWW, KDD, TKDE, and TCSS. My research interests primarily focus on click-through prediction, multimodal data mining, and generative recommender systems:
- Click-through Prediction: Click-through rate prediction is an important task for online advertising and recommendation, which aims to estimate the probability that a user will click on a certain item. Likewise, CTR prediction models have been widely applied to predicting users’ like, favorite, purchase, or download actions. These tasks are usually formulated as a binary classification problem, which incorporates rich but heterogeneous (e.g., numerical, categorical, sequential) features extracted from user profiles, item attributes, and session contexts.
- Multimodal Recommender Systems: Multimodal recommender systems are capable of utilizing multiple types of data (such as text, images, audio, and video) for recommendation. Unlike traditional methods that rely solely on single-modal information, such as user ID, item ID, multimodal recommendation integrates product descriptions, user reviews, images, audio, and other content from different modalities to enhance both the accuracy and diversity of recommendations.
- Generative Recommender Systems: Generative recommender systems leverage auto-encoding models, auto-regressive models, and large language models to generate personalized recommendations. In contrast to traditional recommendation methods that primarily depend on user-item interaction data, generative models introduce novel paradigms for conceptualizing and delivering recommendations, enabling more flexible and creative recommendation strategies.
🔥 News
- 2026.08: 🎉🎉 One paper on a unified Mixture-of-Experts and Transformer architecture for multi-domain recommendation is accepted by the ACM International Conference on Information and Knowledge Management (CIKM’26).
- 2026.06: 🎉🎉 Winning Top 1% (4th) place in the preliminary round of the Tencent Advertising Algorithm Competition 2026.
- 2026.05: 🎉🎉 I have been selected for the 2026 Tencent RhinoBird Elite Talent Program, with my research topic being generative recommender systems.
- 2025.12: 🎉🎉 I am selected for the 2025 China Association for Science and Technology Young Scientist Sponsorship Program (Doctoral Student Special Project).
- 2025.11: 🎉🎉 One paper focusing on a simple yet effective cross network for CTR prediction is accepted by the SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’26).
- 2025.09: 🎉🎉 Winning China National Scholarship (Ranked 1st in the School of Computer Science and Technology, Anhui University, 2025).
- 2025.09: 🎉🎉 Winning Top 1% (14th) place in the preliminary round of the Tencent Advertising Algorithm Competition 2025.
- 2025.05: 🎉🎉 One paper focusing on quadratic neural networks for CTR prediction is accepted by the SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’25).
- 2025.04: 🎉🎉 Winning 2nd Place in the Web Conference 2025 Multimodal CTR Prediction Challenge Track.
📝 Publications
MoEFormer: A Unified Mixture-of-Experts and Transformer Architecture for Multi-Domain Recommendation (CIKM’26, CCF B) [Code]
Honghao Li, Zihan Lin, Jiapeng Xu, Yiwen Zhang, Rui Zhong, et al.
UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction (arXiv 2026) [Code] ★ Star115
Honghao Li, Xianquan Wang, Zibin Zhang, Yi Zhang, Kangyi Lin, Yiwen Zhang.
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction (KDD’26, CCF A) [Code]
Honghao Li, Yiwen Zhang, Yi Zhang, Hanwei Li, Lei Sang, Jieming Zhu.
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction (KDD’25, CCF A) [Code]
Honghao Li, Yiwen Zhang, Yi Zhang, Lei Sang, Jieming Zhu.
SimCEN: Simple Contrast-enhanced Network for CTR Prediction (MM’24, CCF A) [Code]
Honghao Li, Lei Sang, Yi Zhang, Yiwen Zhang.
CETN: Contrast-enhanced Through Network for Click-Through Rate Prediction (TOIS’24, CCF A) [Code]
Honghao Li, Lei Sang, Yi Zhang, Xuyun Zhang, Yiwen Zhang
TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation (TCSS’26, CCF C, JCR Q1) [Code]
Honghao Li, Qiuze Ru, Yiwen Zhang, Yi Zhang, Lei Sang, Yun Yang
AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction (TOIS’24, CCF A) [Code]
Lei Sang, Honghao Li, Yiwen Zhang, Yi Zhang, Yun Yang (Supervisor as First Author)
📝 Other Publications
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TriSAGE: A Tri-subspace Semantic Admission Framework for Multimodal CTR Prediction. Ziyun Chen, Yuhan Wang, Qing Xie, Mengzi Tang, Huping Yu, Honghao Li, Bolong Zheng. (ACM MM’26, CCF A)
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Do We Really Need LLMs to Augment All? A Selective Augmentation Framework with Lightweight Language Models for Multimodal CTR Prediction. Ziyun Chen, Yuhan Wang, Honghao Li, Mengzi Tang, Qing Xie, Yongjian Liu. (RecSys’26, CCF B)
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Towards Similar Alignment and Unique Uniformity in Collaborative Filtering. Lei Sang, Yu Zhang, Yi Zhang, Honghao Li, Yiwen Zhang. Expert Systems with Applications (ESWA’24, CCF C, JCR Q1)
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Dual-domain Collaborative Denoising for Social Recommendation. Wenjie Chen, Yi Zhang, Honghao Li, Lei Sang, Yiwen Zhang (TCSS’24, CCF C, JCR Q1)
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Large Language Model Aided QoS Prediction for Service Recommendation. Huiying Liu, Zekun Zhang, Honghao Li, Qilin Wu, and Yiwen Zhang (ICWS’25, CCF B)
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From Collapse to Stability: A Knowledge-Driven Ensemble Framework for Scaling Up Click-Through Rate Prediction Models. Honghao Li, Lei Sang, Yi Zhang, Guangming Cui, Yiwen Zhang
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Quadratic Interest Network for Multimodal Click-Through Rate Prediction. Honghao Li, Hanwei Li, Jing Zhang, Yi Zhang, Ziniu Yu, Lei Sang, Yiwen Zhang
🎖 Honors and Awards
🏆2025 China Association for Science and Technology Young Scientist Sponsorship Program (Doctoral Student Special Project)
🏆China National Scholarship (Ranked 1st in the School of Computer Science and Technology, Anhui University, 2025)
🏆Second Place Globally in the MMCTR Challenge at The Web Conference 2025 (Multimodal CTR Prediction Challenge Track, Technical Report)
🏆Winning Top 1% (14th) place in the preliminary round of the Tencent Advertising Algorithm Competition 2025.
🏆Winning Top 1% (4th) place in the preliminary round of the Tencent Advertising Algorithm Competition 2026.
📖 Educations
- 2018.09 - 2022.06, Undergraduate student at Bengbu University.
- 2022.09 - 2027.06, Ph.D. student in the combined master’s and doctoral program in the School of Computer Science and Technology at Anhui University.
💻 Internships
- 2025.11 - 2026.02, Research intern at Xiaohongshu.
- 2026.07 - now, Research intern at Tencent WXG.