Fan Liu - RankMe

56 papers A* 12A 1Journal 41Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Variational Rectification Inference for Learning with Noisy Labels. CoRR Haoliang Sun, Qi Wei, Lei Feng, Yupeng Hu, Fan Liu, Hehe Fan, Yilong Yin
2025	—	`book`	Advancing Recommender Systems with Graph Convolutional Networks Fan Liu, Liqiang Nie
2025	—	`conf`	CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG. ACL (1) Yang Tian, Fan Liu, Jingyuan Zhang, Victoria W., Yupeng Hu, Liqiang Nie
2025	J	`jnl`	CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG. CoRR Yang Tian, Fan Liu, Jingyuan Zhang, Victoria W., Yupeng Hu, Liqiang Nie
2025	J	`jnl`	Correction: Variational Rectification Inference for Learning with Noisy Labels. Int. J. Comput. Vis. Haoliang Sun, Qi Wei, Lei Feng, Yupeng Hu, Fan Liu, Hehe Fan, Yilong Yin
2025	J	`jnl`	Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation. CoRR Han Liu, Yinwei Wei, Fan Liu, Wenjie Wang, Liqiang Nie, Tat-Seng Chua
2025	A*	`conf`	Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models. ACM Multimedia Yu-Wei Zhan, Fan Liu, Xin Luo, Xin-Shun Xu, Liqiang Nie, Mohan Kankanhalli
2025	A*	`conf`	I ACM Multimedia Huilin Chen, Miaomiao Cai, Fan Liu, Zhiyong Cheng, Richang Hong, Meng Wang
2025	J	`jnl`	I CoRR Huilin Chen, Miaomiao Cai, Fan Liu, Zhiyong Cheng, Richang Hong, Meng Wang
2025	J	`jnl`	Open Multimodal Retrieval-Augmented Factual Image Generation. CoRR Yang Tian, Fan Liu, Jingyuan Zhang, Wei Bi, Yupeng Hu, Liqiang Nie
2025	J	`jnl`	Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models. ACM Trans. Inf. Syst. Fan Liu, Yaqi Liu, Huilin Chen, Zhiyong Cheng, Liqiang Nie, Mohan S. Kankanhalli
2025	J	`jnl`	User Invariant Preference Learning for Multi-Behavior Recommendation. CoRR Mingshi Yan, Zhiyong Cheng, Fan Liu, Yingda Lyu, Yahong Han
2025	J	`jnl`	User Invariant Preference Learning for Multi-Behavior Recommendation. ACM Trans. Inf. Syst. Mingshi Yan, Zhiyong Cheng, Fan Liu, Yingda Lyu, Yahong Han
2025	J	`jnl`	Variational Rectification Inference for Learning with Noisy Labels. Int. J. Comput. Vis. Haoliang Sun, Qi Wei, Lei Feng, Yupeng Hu, Fan Liu, Hehe Fan, Yilong Yin
2024	A*	`conf`	Attribute-driven Disentangled Representation Learning for Multimodal Recommendation. ACM Multimedia Zhenyang Li, Fan Liu, Yinwei Wei, Zhiyong Cheng, Liqiang Nie, Mohan S. Kankanhalli
2024	A*	`conf`	Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. NeurIPS Zhao Xu, Fan Liu, Hao Liu
2024	A*	`conf`	Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation. SIGIR Mingshi Yan, Fan Liu, Jing Sun, Fuming Sun, Zhiyong Cheng, Yahong Han
2024	J	`jnl`	Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation. CoRR Mingshi Yan, Fan Liu, Jing Sun, Fuming Sun, Zhiyong Cheng, Yahong Han
2024	J	`jnl`	Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation. ACM Trans. Inf. Syst. Mingshi Yan, Zhiyong Cheng, Chen Gao, Jing Sun, Fan Liu, Fuming Sun, Haojie Li
2024	J	`jnl`	Cluster-Based Graph Collaborative Filtering. ACM Trans. Inf. Syst. Fan Liu, Shuai Zhao, Zhiyong Cheng, Liqiang Nie, Mohan S. Kankanhalli
2024	J	`jnl`	Cluster-based Graph Collaborative Filtering. CoRR Fan Liu, Shuai Zhao, Zhiyong Cheng, Liqiang Nie, Mohan S. Kankanhalli
2024	J	`jnl`	Decoupled domain-specific and domain-conditional representation learning for cross-domain recommendation. Inf. Process. Manag. Yu Zhang, Zhiyong Cheng, Fan Liu, Xun Yang, Yuxin Peng
2024	J	`jnl`	Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation. CoRR Zhiyong Cheng, Jianhua Dong, Fan Liu, Lei Zhu, Xun Yang, Meng Wang
2024	J	`jnl`	Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation. Trans. Recomm. Syst. Zhiyong Cheng, Jianhua Dong, Fan Liu, Lei Zhu, Xun Yang, Meng Wang
2024	J	`jnl`	Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation. ACM Trans. Inf. Syst. Han Liu, Yinwei Wei, Fan Liu, Wenjie Wang, Liqiang Nie, Tat-Seng Chua
2024	J	`jnl`	Learning to Agree on Vision Attention for Visual Commonsense Reasoning. IEEE Trans. Multim. Zhenyang Li, Yangyang Guo, Kejie Wang, Fan Liu, Liqiang Nie, Mohan S. Kankanhalli
2024	J	`jnl`	Open-Set Synthesis for Free-Viewpoint Human Body Reenactment of Novel Poses. IEEE Trans. Circuits Syst. Video Technol. Zhicheng Sheng, Fan Liu, Meng Liu, Feng Zheng, Liqiang Nie
2023	A*	`conf`	Advancing Video Question Answering with a Multi-modal and Multi-layer Question Enhancement Network. ACM Multimedia Meng Liu, Fenglei Zhang, Xin Luo, Fan Liu, Yinwei Wei, Liqiang Nie
2023	J	`jnl`	Attribute-driven Disentangled Representation Learning for Multimodal Recommendation. CoRR Zhenyang Li, Fan Liu, Yinwei Wei, Zhiyong Cheng, Liqiang Nie, Mohan S. Kankanhalli
2023	J	`jnl`	CoGCN: co-occurring item-aware GCN for recommendation. Neural Comput. Appl. Xinxiao Zhao, Fan Liu, Hao Liu, Mingzhu Xu, Haoyu Tang, Xueqing Li, Yupeng Hu
2023	J	`jnl`	Deep Temporal State Perception Toward Artificial Cyber-Physical Systems. IEEE Internet Things J. Yan Jin, Shaokun Wang, Fan Liu, Hongyun Fan, Yupeng Hu, Xueqing Li, Shijun Liu
2023	J	`jnl`	Disentangled Graph Neural Networks for Session-Based Recommendation. IEEE Trans. Knowl. Data Eng. Ansong Li, Zhiyong Cheng, Fan Liu, Zan Gao, Weili Guan, Yuxin Peng
2023	J	`jnl`	Disentangled Multimodal Representation Learning for Recommendation. IEEE Trans. Multim. Fan Liu, Huilin Chen, Zhiyong Cheng, Anan Liu, Liqiang Nie, Mohan S. Kankanhalli
2023	J	`jnl`	Dual attentive graph convolutional networks for cross-domain recommendation. J. Intell. Fuzzy Syst. Yu Zhang, Fan Liu, Yupeng Hu, Xiaoli Li, Xiangjun Dong, Zhiyong Cheng
2023	J	`jnl`	Generating Human-Centric Visual Cues for Human-Object Interaction Detection via Large Vision-Language Models. CoRR Yu-Wei Zhan, Fan Liu, Xin Luo, Liqiang Nie, Xin-Shun Xu, Mohan S. Kankanhalli
2023	J	`jnl`	Learning to Agree on Vision Attention for Visual Commonsense Reasoning. CoRR Zhenyang Li, Yangyang Guo, Kejie Wang, Fan Liu, Liqiang Nie, Mohan S. Kankanhalli
2023	A*	`conf`	LightGT: A Light Graph Transformer for Multimedia Recommendation. SIGIR Yinwei Wei, Wenqi Liu, Fan Liu, Xiang Wang, Liqiang Nie, Tat-Seng Chua
2023	A*	`conf`	Multi-Behavior Recommendation with Cascading Graph Convolution Networks. WWW Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu, Zan Gao, Yuxin Peng
2023	J	`jnl`	Multi-Behavior Recommendation with Cascading Graph Convolution Networks. CoRR Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu, Zan Gao, Yuxin Peng
2023	A	`conf`	Multi-queue Momentum Contrast for Microvideo-Product Retrieval. WSDM Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie
2023	A*	`conf`	Semantic-Guided Feature Distillation for Multimodal Recommendation. ACM Multimedia Fan Liu, Huilin Chen, Zhiyong Cheng, Liqiang Nie, Mohan S. Kankanhalli
2023	J	`jnl`	Semantic-Guided Feature Distillation for Multimodal Recommendation. CoRR Fan Liu, Huilin Chen, Zhiyong Cheng, Liqiang Nie, Mohan S. Kankanhalli
2023	J	`jnl`	Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models. CoRR Fan Liu, Yaqi Liu, Huilin Chen, Zhiyong Cheng, Liqiang Nie, Mohan S. Kankanhalli
2022	J	`jnl`	An Attribute-Aware Attentive GCN Model for Attribute Missing in Recommendation. IEEE Trans. Knowl. Data Eng. Fan Liu, Zhiyong Cheng, Lei Zhu, Chenghao Liu, Liqiang Nie
2022	J	`jnl`	Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation. CoRR Mingshi Yan, Zhiyong Cheng, Chen Gao, Jing Sun, Fan Liu, Fuming Sun, Haojie Li
2022	J	`jnl`	Disentangled Graph Neural Networks for Session-based Recommendation. CoRR Ansong Li, Zhiyong Cheng, Fan Liu, Zan Gao, Weili Guan, Yuxin Peng
2022	J	`jnl`	Disentangled Multimodal Representation Learning for Recommendation. CoRR Fan Liu, Zhiyong Cheng, Huilin Chen, Anan Liu, Liqiang Nie, Mohan S. Kankanhalli
2022	J	`jnl`	Feature-Level Attentive ICF for Recommendation. ACM Trans. Inf. Syst. Zhiyong Cheng, Fan Liu, Shenghan Mei, Yangyang Guo, Lei Zhu, Liqiang Nie
2022	J	`jnl`	Multi-queue Momentum Contrast for Microvideo-Product Retrieval. CoRR Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie
2022	A*	`conf`	Privacy-Preserving Synthetic Data Generation for Recommendation Systems. SIGIR Fan Liu, Zhiyong Cheng, Huilin Chen, Yinwei Wei, Liqiang Nie, Mohan S. Kankanhalli
2022	J	`jnl`	Privacy-Preserving Synthetic Data Generation for Recommendation Systems. CoRR Fan Liu, Zhiyong Cheng, Huilin Chen, Yinwei Wei, Liqiang Nie, Mohan S. Kankanhalli
2021	A*	`conf`	Interest-aware Message-Passing GCN for Recommendation. WWW Fan Liu, Zhiyong Cheng, Lei Zhu, Zan Gao, Liqiang Nie
2021	J	`jnl`	Interest-aware Message-Passing GCN for Recommendation. CoRR Fan Liu, Zhiyong Cheng, Lei Zhu, Zan Gao, Liqiang Nie
2020	J	`jnl`	A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation. CoRR Fan Liu, Zhiyong Cheng, Lei Zhu, Chenghao Liu, Liqiang Nie
2019	A*	`conf`	User Diverse Preference Modeling by Multimodal Attentive Metric Learning. ACM Multimedia Fan Liu, Zhiyong Cheng, Changchang Sun, Yinglong Wang, Liqiang Nie, Mohan S. Kankanhalli
2019	J	`jnl`	User Diverse Preference Modeling by Multimodal Attentive Metric Learning. CoRR Fan Liu, Zhiyong Cheng, Changchang Sun, Yinglong Wang, Liqiang Nie, Mohan S. Kankanhalli

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.