Publications

Yuxin Chen, Junfei Tan, An Zhang, Zhengyi Yang, leheng sheng, Enzhi Zhang, Xiang Wang, Tat-Seng Chua (2024). On Softmax Direct Preference Optimization for Recommendation. In NeurIPS'24.

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Junfeng Fang, Zongze Bi, Ruipeng Wang, houchenjiang, yuangao, An Zhang, Jie Shi, Xiang Wang, Tat-Seng Chua (2024). Towards Neuron Attributions in Multi-Modal Large Language Models. In NeurIPS'24.

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Xingyu Zhu, Beier Zhu, yitan, Shuo Wang, Yanbin Hao, Hanwang Zhang (2024). Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting. In NeurIPS'24.

Xiaoyu Kong, Jiancan Wu, An Zhang, leheng sheng, hui lin, Xiang Wang, Xiangnan He (2024). Customizing Language Models with Instance-wise LoRA for Sequential Recommendation. In NeurIPS'24.

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Junkang Wu, Yuexiang Xie, Zhengyi Yang, Jiancan Wu, Jinyang Gao, Bolin Ding, Xiang Wang, Xiangnan He (2024). $\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$. In NeurIPS'24.

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Shihao Cai, Keqin Bao, Jizhi Zhang, Hangyu Guo, Jun Song, Bo Zheng (2024). GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation. In EMNLP'24.

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Wentao Shi, Mengqi Yuan, Junkang Wu, Qifan Wang, Fuli Feng (2024). Direct Multi-Turn Preference Optimization for Language Agents. In EMNLP'24.

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Keqin Bao, Jizhi Zhang, Zhang Yang, Xinyue Huo, Chong Chen, Fuli Feng (2024). Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation. In EMNLP'24.

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Muchen Yang, Moxin Li, Yongle Li, Zijun Chen, Chongming Gao, Junqi Zhang, Yangyang Li, Fuli Feng (2024). Dual-Phase Accelerated Prompt Optimization. In EMNLP'24.

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Xingyu Zhu, Beier Zhu, yitan, Shuo Wang, Yanbin Hao, Hanwang Zhang (2024). Selective Vision-Language Subspace Projection for Few-shot CLIP. In ACM MM'24.

Gangyi Zhang, Chongming Gao, Hang Pan, Runzhe Teng, Ruizhe Li (2024). Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning. In CIKM'24.

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Jinqiu Jin, Haoxuan Li, Fuli Feng (2024). On the Maximal Local Disparity of Fairness-Aware Classifiers. In ICML'24.

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Kexin Huang, Ziqian Chen, Xue Wang, Chongming Gao, Jinyang Gao, Bolin Ding, Xiang Wang (2024). Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games. In ICML'24.

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Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu, Jianxiong Wei, Qingsong Hua (2024). Treatment Effect Estimation for User Interest Exploration on Recommender Systems. In SIGIR'24.

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Zihao Zhao, yijing, Fuli Feng, Jiancan Wu, Chongming Gao, Xiangnan He (2024). Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients. In SIGIR 2024.

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Wentao Shi, Xiangnan He, yangzhang, Chongming Gao, xinyueli, Jizhi Zhang, qifanwang, Fuli Feng (2024). Large Language Models are Learnable Planners for Long-Term Recommendation. In SIGIR 2024.

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Tianhao Shi, yangzhang, Jizhi Zhang, Fuli Feng, Xiangnan He (2024). Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach. In SIGIR 2024.

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Yiyan Xu, wenjiewang, Fuli Feng, yunshanma, Jizhi Zhang, Xiangnan He (2024). Diffusion Models for Generative Outfit Recommendation. In SIGIR 2024.

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Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang, Dingxian Wang, Maarten de Rijke (2024). Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning. In SIGIR 2024.

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Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, Xiangnan He (2024). Large Language-Recommendation Assistant. In SIGIR'24.

Yuan Gao, Jinghan Li, Xiang Wang, Xiangnan He, fenghuamin, zhangyongdong (2024). Revisiting Attack-caused Structural Distribution Shift in Graph Anomaly Detection. In TKDE.

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Xinyuan Zhu, xiaohanren, Wentao Shi, Changming Wang, Xuehan Liu, Yuqing Liu, Tao Tao, Fuli Feng (2024). Improving Prostate Cancer Risk Prediction through Partial AUC Optimization. WWW ’24.

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Jinqiu Jin, Sihao Ding, Wenjie Wang, Fuli Feng (2024). Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction. In WWW 2024 (short).

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Shuxian Bi, wenjiewang, Hang Pan, Fuli Feng, Xiangnan He (2024). Proactive Recommendation with Iterative Preference Guidance. In WWW 2024.

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Wentao Shi, chenxuwang, Fuli Feng, yangzhang, wenjiewang, Junkang Wu, Xiangnan He (2024). Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation. In WWW 2024.

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Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He (2024). Item-side Fairness of Large Language Model-based Recommendation System. In WWW 2024.

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Junfeng Fang, Xinlin Li, yongduosui, yuangao, Guibin Zhang, Kun Wang, Xiang Wang, Xiangnan He (2024). EXGC: Bridging Efficiency and Explainability in Graph Condensation. In WWW 2024.

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yongduosui, Caizhi Tang, Zhixuan Chu, Junfeng Fang, yuangao, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang (2024). Invariant Graph Learning for Causal Effect Estimation. In WWW'24.

Yuan Gao, Junfeng Fang, Yongduo Sui, liyangyang, Xiang Wang, fenghuamin, zhangyongdong (2024). Graph Anomaly Detection with Bi-level Optimization. In WWW 2024.

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haoxuanli, chunyuanzheng, sihaoding, Fuli Feng, Xiangnan He, zhigeng, pengwu (2024). Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference for Recommendation. In ICLR 2024.

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Sihang Li, Liu Zhiyuan, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, Qi Tian (2024). 3D-MoLM: Towards 3D Molecule-Text Interpretation in Language Models. In ICLR'24.

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Jiayi Liao, Xu Chen, Qiang Fu, Lun Du, Xiangnan He, Xiang Wang, Shi Han, Dongmei Zhang (2023). Text-to-Image Generation for Abstract Concepts. In AAAI'24.

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Xingyu Zhu, Shuo Wang, Jinda Lu, Yanbin Hao, haifengliu, Xiangnan He (2023). Boosting Few-shot Learning via Attentive Feature Regularization. In AAAI 2024.

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Yanchen Luo, Sihang Li, yongduosui, Junkang Wu, Jiancan Wu, Xiang Wang (2023). Masked Graph Modeling with Multi-View Contrast. In ICDE'24.

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Junkang Wu, Jiawei Chen, Jiancan Wu, Wentao Shi, Jizhi Zhang, Xiang Wang (2023). BSL: Understanding and Improving Softmax Loss for Recommendation. In ICDE'24.

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Yimeng Bai, Yang Zhang, Jing Lu, Jianxin Chang, Xiaoxue Zang, Yanan Niu, Yang Song, Fuli Feng (2023). LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting. In WSDM'24.

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yongduosui, qitianwu, Jiancan Wu, qingcui, longfeili, junzhou, Xiang Wang, Xiangnan He (2023). Unleashing the Power of Graph Data Augmentation on Covariate Shift. In NeurIPS 2023.

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Junkang Wu, jiaweichen, Jiancan Wu, Wentao Shi, Xiang Wang, Xiangnan He (2023). Understanding Contrastive Learning via Distributionally Robust Optimization. In NeurIPS 2023.

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haoxuanli, kunhanwu, chunyuanzheng, yanghaoxiao, haowang, Fuli Feng, Xiangnan He, zhigeng, pengwu (2023). Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach. In NeurIPS 2023.

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Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, yanchengyuan, Xiangnan He (2023). Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion. In NeurIPS 2023.

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Jinqiu Jin, haoxuanli, Fuli Feng, sihaoding, pengwu, Xiangnan He (2023). Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach. In NeurIPS 2023.

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Junfeng Fang, Wei Liu, Xiang Wang, zeminliu, anzhang, yuangao, Xiangnan He (2023). Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis. In NeurIPS 2023.

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Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu, Yang Song (2023). Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework. In CIKM'23.

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Keqin Bao, Jizhi Zhang, yangzhang, wenjiewang, Fuli Feng, Xiangnan He (2023). TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In Recsys 2023.

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Jizhi Zhang, Keqin Bao, yangzhang, wenjiewang, Fuli Feng, Xiangnan He (2023). Can ChatGPT Make Fair Recommendation? A Fairness Evaluation Benchmark for Recommendation with Large Language Model. In Recsys 2023.

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Jinda Lu, Shuo Wang, xinyuzhang, Yanbin Hao, Xiangnan He (2023). Semantic-based Selection, Synthesis, and Supervision for Few-shot Learning. In ACM MM 2023.

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jiaruiyu, Haoran Li, Yanbin Hao, binzhu, tongxu, Xiangnan He (2023). CgT-GAN: CLIP-guided Text GAN for Image Captioning. In ACM MM 2023.

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changshengwang, jianbaiye, wenjiewang, Chongming Gao, Fuli Feng, Xiangnan He (2023). RecAD: Towards A Unified Library for Recommender Attack and Defense. In Recsys 2023.

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Jiancan Wu, Xiang Wang, xingyugao, jiaweichen, hongchengfu, tianyuqiu, Xiangnan He (2023). On the Effectiveness of Sampled Softmax Loss for Item Recommendation. In ACM Transactions on Information Systems (TOIS, major revision).

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Chongming Gao, wenqianglei, jiaweichen, siqiwang, Xiangnan He, shijunli, biaoli, yuanzhang, pengjiang (2023). CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System. In ACM Transactions on Information Systems.

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Hang Pan, jiaweichen, Fuli Feng, Wentao Shi, Junkang Wu, Xiangnan He (2023). Discriminative-Invariant Representation Learning for Unbiased Recommendation. In IJCAI 2023.

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sanzhang, San Zhang (2023). Title of Your Paper. In SIGIR'23 Best Paper Honorable Mention.

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yangzhang, Tianhao Shi, Fuli Feng, wenjiewang, dingxianwang, Xiangnan He, yongdongzhang (2023). Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation. In SIGIR 2023.

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Yulong Huang, yangzhang, qifanwang, chenxuwang, Fuli Feng (2023). Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation. In SIGIR 2023.

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wenjiewang, Yiyan Xu, Fuli Feng, xinyulin, Xiangnan He, tat-sengchua (2023). Diffusion Recommender Model. In SIGIR 2023.

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Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng jiang, Shiqi Wang, Zhong Zhang, Xiangnan He (2023). Alleviating Matthew Effect of Offline Reinforcement Learning in Recommendation. In SIGIR'23 Best Paper Honorable Mention.

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Zhengyi Yang, Xiangnan He, Jizhi Zhang, Jiancan Wu, xinxin, jiaweichen, Xiang Wang (2023). A Generic Learning Framework for Sequential Recommendation with Distribution Shifts. In SIGIR 2023.

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Xun Deng, Fuli Feng, Xiang Wang, Xiangnan He, hanwangzhang, tat-sengchua (2023). Learning to Double-check Model Prediction from a Causal Perspective. In IEEE Transactions on Neural Networks and Learning Systems.

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Zhicai Wang, Yanbin Hao, tingtingmu, Ouxiang Li, Shuo Wang, Xiangnan He (2023). Bi-directional Distribution Alignment for Transductive Zero Shot Learning. In CVPR 2023.

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Xun Deng, wenjiewang, Fuli Feng, hanwangzhang, Xiangnan He, yongliao (2023). Counterfactual Active Learning for Out-of-Distribution Generalization. In ACL 2023.

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gangchen, jiaweichen, Fuli Feng, shengzhou, Xiangnan He (2023). Unbiased Knowledge Distillation for Recommendation. In WSDM 2023.

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Junfeng Fang, Xiang Wang, anzhang, zeminliu, Xiangnan He, tat-sengchua (2023). Cooperative Explanations of Graph Neural Networks. In WSDM 2023.

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yuangao, Xiang Wang, Xiangnan He, zhenguangliu, huaminfeng, yongdongzhang (2023). Alleviating Structural Distribution Shift in Graph Anomaly Detection. In WSDM 2023.

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Zhicai Wang, Longhui Wei, Tan Wang, Heyu Chen, Yanbin Hao, Xiang Wang, Xiangnan He, Qi Tian (2023). Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model. In CVPR 2024.

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Meng Jiang, yangzhang, yuangao, Yansong Wang, Fuli Feng, Xiangnan He (2023). LightMIRM: Light Meta-learned Invariant Risk Minimization for Trustworthy Loan Default Prediction. In ICDE'23.

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Wentao Shi, jiaweichen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, Xiangnan He (2023). On the Theories Behind Hard Negative Sampling for Recommendation. In WWW 2023.

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Jiancan Wu, yiyang, yuchunqian, yongduosui, Xiang Wang, Xiangnan He (2023). GIF: A General Graph Unlearning Strategy via Influence Function. In WWW 2023.

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yuangao, Xiang Wang, Xiangnan He, zhenguangliu, huaminfeng, yongdongzhang (2023). Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum. In WWW 2023.

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jiaweichen, Junkang Wu, Jiancan Wu, xuezhicao, shengzhou, Xiangnan He (2023). Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation. In WWW 2023.

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changyixiao, Xiangnan He, yixincao (2023). Knowledge Graph Embedding by Normalizing Flows. In AAAI 2023.

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Zihao Zhao, jiaweichen, shengzhou, Xiangnan He, xuezhicao, fuzhengzhang, weiwu (2023). Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation. In IEEE Transactions on Knowledge and Data Engineering.

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Xinyuan Zhu, yangzhang, Fuli Feng, xunyang, dingxianwang, Xiangnan He (2023). Mitigating Hidden Confounding Effects for Causal Recommendation. In IEEE Transactions on Knowledge and Data Engineering (TKDE, major revision).

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yongduosui, Xiang Wang, tianlongchen, mengwang, Xiangnan He, tat-sengchua (2023). Inductive Lottery Ticket Learning for Graph Neural Networks. In Journal of Computer Science and Technology.

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Chongming Gao, shijunli, wenqianglei, jiaweichen, biaoli, pengjiang, Xiangnan He, jiaxinmao, tat-sengchua (2022). KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems. In CIKM 2022.

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Chongming Gao, shijunli, yuanzhang, jiaweichen, biaoli, wenqianglei, pengjiang, Xiangnan He (2022). KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. In CIKM 2022.

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Yanbin Hao, Jingru Duan, haozhang, binzhu, pengyuanzhou, Xiangnan He (2022). Unsupervised Video Hashing with Multi-granularity Contextualization and Multi-structure Preservation. In ACM MM 2022.

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Zhicai Wang, Yanbin Hao, xingyugao, haozhang, Shuo Wang, tingtingmu, Xiangnan He (2022). Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP. In ACM MM 2022.

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Shuo Wang, xinyuzhang, Yanbin Hao, Chengbing Wang, Xiangnan He (2022). Multi-directional Knowledge Transfer for Few-Shot Learning. In ACM MM 2022.

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xiaoyudu, zikewu, Fuli Feng, Xiangnan He, jinhuitang (2022). Invariant Representation Learning for Multimedia Recommendation. In ACM MM 2022.

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yitan, Yanbin Hao, haozhang, Shuo Wang, Xiangnan He (2022). Hierarchical Hourglass Convolutional Network for Efficient Video Classification. In ACM MM 2022.

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yanfangwang, yongduosui, Xiang Wang, zhenguangliu, Xiangnan He (2022). Exploring Lottery Ticket Hypothesis in Media Recommender Systems. In International Journal of Intelligent Systems.

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yongduosui, Xiang Wang, Jiancan Wu, minlin, Xiangnan He, tat-sengchua (2022). Causal Attention for Interpretable and Generalizable Graph Classification. In KDD 2022.

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sihaoding, pengwu, Fuli Feng, yitongwang, Xiangnan He, yongliao, yongdongzhang (2022). Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis. In KDD 2022.

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Sihang Li, Xiang Wang, anzhang, ying-xinwu, Xiangnan He, tat-sengchua (2022). Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. In ICML 2022.

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Xiang Wang, yingxinwu, anzhang, Fuli Feng, Xiangnan He, tat-sengchua (2022). Reinforced Causal Explainer for Graph Neural Networks. In IEEE Transactions on Pattern Analysis and Machine Intelligence.

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sihaoding, Fuli Feng, Xiangnan He, Jinqiu Jin, wenjiewang, yongliao, yongdongzhang (2022). Interpolative Distillation for Unifying Biased and Debiased Recommendation. In SIGIR 2022.

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Yanbin Hao, haozhang, chong-wahngo, Xiangnan He (2022). Group Contextualization for Video Recognition. In CVPR 2022.

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moxinli, Fuli Feng, hanwangzhang, Xiangnan He, fengbinzhu, tat-sengchua (2022). Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning. In ACL 2022.

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Yuyue Zhao, Xiang Wang, jiaweichen, Wei Tang, yashenwang, Xiangnan He, haiyongxie (2022). Time-aware Path Reasoning on Knowledge Graph for Recommendation. In ACM Transactions on Information Systems 2022.

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yuangao, Xiang Wang, Xiangnan He, huaminfeng, yongdongzhang (2022). Rumor Detection with Self-supervised Learning on Texts and Social Graph. In Frontiers of Computer Science.

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chengao, yuzheng, nianli, yinfengli, yingrongqin, jinghuapiao, yuhanquan, jianxinchang, depengjin, Xiangnan He, yongli (2022). Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. In ACM Transactions on Recommender Systems.

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Jiancan Wu, Xiangnan He, Xiang Wang, qifanwang, weijianchen, jianxunlian, xingxie (2022). Graph Convolution Machine for Context-aware Recommender System. In Frontiers of Computer Science.

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jiajiachen, xinxin, xianfengliang, Xiangnan He, junliu (2022). GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation. In IEEE Transactions on Knowledge and Data Engineering.

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chengao, yuzheng, wenjiewang, Fuli Feng, Xiangnan He, yongli (2022). Causal Inference in Recommender Systems: A Survey and Future Directions. In ACM Transactions on Information Systems 2022.

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sihaoding, Fuli Feng, Xiangnan He, yongliao, junshi, yongdongzhang (2022). Causal Incremental Graph Convolution for Recommender System Retraining. In IEEE Transactions on Neural Networks and Learning Systems.

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weijianchen, Fuli Feng, qifanwang, Xiangnan He, chonggangsong, guohuiling, yongdongzhang (2022). CatGCN: Graph Convolutional Networks with Categorical Node Features. In IEEE Transactions on Knowledge and Data Engineering.

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jiaweichen, handedong, Xiang Wang, Fuli Feng, mengwang, Xiangnan He (2022). Bias and Debias in Recommender System: A Survey and Future Directions. In ACM Transactions on Information Systems.

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Yanbin Hao, Shuo Wang, peicao, xinjiangao, tongxu, jinmengwu, Xiangnan He (2022). Attention in Attention: Modeling Context Correlation for Efficient Video Classification. In IEEE Transactions on Circuits and Systems for Video Technology.

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Xiangnan He, yangzhang, Fuli Feng, chonggangsong, linglingyi, guohuiling, yongdongzhang (2022). Addressing Confounding Feature Issue for Causal Recommendation. In ACM Transactions on Information Systems 2022.

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lewu, Xiangnan He, Xiang Wang, kunzhang, mengwang (2022). A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation. In IEEE Transactions on Knowledge and Data Engineering.

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ying-xinwu, Xiang Wang, anzhang, Xiangnan He, tat-sengchua (2022). Discovering Invariant Rationales for Graph Neural Networks. In ICLR 2022.

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yuwang, xinxin, zaiqiaomeng, jeomanjose, Fuli Feng, Xiangnan He (2022). Learning Robust Recommenders through Cross-Model Agreement. In WWW 2022.

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diancheng, jiaweichen, wenjunpeng, wenqinye, fuyulv, taozhuang, xiaoyizeng, Xiangnan He (2022). Interactive Hypergraph Neural Network for Personalized Product Search. In WWW 2022.

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qiwan, Xiangnan He, Xiang Wang, Jiancan Wu, weiguo, ruimingtang (2022). Cross Pairwise Ranking for Unbiased Item Recommendation. In WWW 2022.

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wenjiewang, xinyulin, Fuli Feng, Xiangnan He, minlin, tat-sengchua (2022). Causal Representation Learning for Out-of-Distribution Recommendation. In WWW 2022.

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Xiang Wang, yingxinwu, anzhang, Xiangnan He, tat-sengchua (2021). Towards Multi-Grained Explainability for Graph Neural Networks. In NeurIPS 2021.

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Junkang Wu, Wentao Shi, xuezhicao, jiaweichen, wenqianglei, fuzhengzhang, weiwu, Xiangnan He (2021). DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network. In CIKM 2021.

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daizongding, mizhang, hanruiwang, xudongpan, minyang, Xiangnan He (2021). A Deep Learning Framework for Self-evolving Hierarchical Community Detection. In CIKM 2021.

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yitan, Yanbin Hao, Xiangnan He, yinweiwei, xunyang (2021). Selective Dependency Aggregation for Action Classification. In MM 2021.

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tianxinwei, Fuli Feng, jiaweichen, ziweiwu, jinfengyi, Xiangnan He (2021). Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System. In KDD 2021.

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wenjiewang, Fuli Feng, Xiangnan He, Xiang Wang, tat-sengchua (2021). Deconfounded Recommendation for Alleviating Bias Amplification. In KDD 2021.

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Fuli Feng, weiranhuang, xinxin, Xiangnan He, tat-sengchua, qifanwang (2021). Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method. In SIGIR 2021.

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Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, liangchen, jianxunlian, xingxie (2021). Self-supervised Graph Learning for Recommendation. In SIGIR 2021.

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wenjiewang, Fuli Feng, Xiangnan He, hanwangzhang, tat-sengchua (2021). Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue. In SIGIR 2021.

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yangzhang, Fuli Feng, Xiangnan He, tianxinwei, chonggangsong, guohuiling, yongdongzhang (2021). Causal Intervention for Leveraging Popularity Bias in Recommendation. In SIGIR 2021 Best Paper Honorable Mention.

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jiaweichen, handedong, yangqiu, Xiangnan He, xinxin, liangchen, gulilin, kepingyang (2021). AutoDebias: Learning to Debias for Recommendation. In SIGIR 2021.

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wenjiewang, Fuli Feng, Xiangnan He, liqiangnie, tat-sengchua (2021). Denoising Implicit Feedback for Recommendation. In WSDM 2021.

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shijunli, wenqianglei, qingyunwu, Xiangnan He, pengjiang, tat-sengchua (2021). Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users. In ACM Transactions on Information Systems.

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Fuli Feng, Xiangnan He, hanwangzhang, tat-sengchua (2021). Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions. In IEEE Transactions on Knowledge and Data Engineering.

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jintangli, taoxie, liangchen, fenfangxie, Xiangnan He, zibinzeng (2021). Adversarial Attack on Large Scale Graph. In IEEE Transactions on Knowledge and Data Engineering.

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handedong, jiaweichen, Fuli Feng, Xiangnan He, Shuxian Bi, zhaolinding, pengcui (2021). On the Equivalence of Decoupled Graph Convolution Network and Label Propagation. In WWW 2021.

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Xiang Wang, tinglinhuang, dingxianwang, yanchengyuan, zhenguangliu, Xiangnan He, tat-sengchua (2021). Learning Intents behind Interactions with Knowledge Graph for Recommendation. In WWW 2021.

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yuzheng, chengao, xiangli, Xiangnan He, yongli, depengjin (2021). Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In WWW 2021.

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shunyujiang, Fuli Feng, weijianchen, xiangli, Xiangnan He (2021). Structure-Enhanced Meta-Learning For Few-Shot Graph Classification. In AI Open 2021.

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Chongming Gao, wenqianglei, Xiangnan He, maartenderijke, tat-sengchua (2021). Advances and Challenges in Conversational Recommender Systems: A Survey. In AI Open 2021.

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wenqianglei, Gangyi Zhang, Xiangnan He, yisongmiao, Xiang Wang, liangchen, tat-sengchua (2020). Interactive Path Reasoning on Graph for Conversational Recommendation. In KDD 2020.

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fajieyuan, Xiangnan He, alexandroskaratzoglou, liguangzhang (2020). Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. In SIGIR 2020.

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Xiangnan He, kuandeng, Xiang Wang, yanli, yongdongzhang, mengwang (2020). LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR 2020.

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yangzhang, Fuli Feng, chenxuwang, Xiangnan He, mengwang, yanli, yongdongzhang (2020). How to Retrain Recommender System? A Sequential Meta-Learning Approach. In SIGIR 2020.

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jianxinchang, chengao, Xiangnan He, depengjin, yongli (2020). Bundle Recommendation with Graph Convolutional Networks. In SIGIR 2020 Best Short Paper Honorable Mention.

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hongminzhu, Fuli Feng, Xiangnan He, Xiang Wang, yanli, kaizheng, yongdongzhang (2020). Bilinear Graph Neural Network with Neighbor Interactions. In IJCAI 2020.

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daizongding, mizhang, xudongpan, minyang, Xiangnan He (2020). Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning. In AAAI 2020.

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Fuli Feng, Xiangnan He, jietang, tat-sengchua (2020). Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure. In IEEE Transactions on Knowledge and Data Engineering.

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Xiang Wang, yaokunxu, Xiangnan He, yixincao, wengwang, tat-sengchua (2020). Reinforced Negative Sampling over Knowledge Graph for Recommendation. In WWW 2020.

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fajieyuan, Xiangnan He, haochuanjiang, guibingguo, jianxiong, zhezhaoxu, xiongyilin (2020). Future Data Helps Training: Modelling Future Contexts for Session-based Recommendation. In WWW 2020.

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wenqianglei, Xiangnan He, yisongmiao, qingyunwu, richanghong, min-yenkan, tat-sengchua (2020). Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. In WSDM 2020.

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junxu, Xiangnan He, hangli (2020). Deep Learning for Matching in Search and Recommendation. In Foundations and Trends in Information Retrieval 2020.

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yihongchen, beichen, Xiangnan He, chengao, yongli, jian-guanglou, yuewang (2019). λOpt: Learn to Regularize Recommender Models in Finer Levels. In KDD 2019.

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daizongding, mizhang, xudongpan, minyang, Xiangnan He (2019). Modeling Extreme Events in Time Series Prediction. In KDD 2019.

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Xiang Wang, Xiangnan He, yixincao, mengliu, tat-sengchua (2019). KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.

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xinxin, Xiangnan He, yongfengzhang, yongdongzhang, joemonjose (2019). Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. In SIGIR 2019.

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Xiang Wang, Xiangnan He, mengwang, Fuli Feng, tat-sengchua (2019). Neural Graph Collaborative Filtering. In SIGIR 2019.

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weijianchen, yulonggu, zhaochunren, Xiangnan He, hongtaoxie, tongguo, daweiyin., yongdongzhang (2019). Semi-supervised User Profiling with Heterogeneous Graph Attention Networks. In IJCAI 2019.

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yixincao, Xiang Wang, Xiangnan He, zikunhu, tat-sengchua (2019). Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference. In WWW 2019.

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Xiang Wang, dingxianwang, canranxu, Xiangnan He, yixincao, tat-sengchua (2019). Explainable Reasoning over Knowledge Graph Paths for Recommendation. In AAAI 2019.

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Xiangnan He, jinhuitang, xiaoyudu, richanghong, tongweiren, tat-sengchua (2019). Fast Matrix Factorization with Non-Uniform Weights on Missing Data. In IEEE Transactions on Neural Networks and Learning Systems.

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fajieyuan, alexandroskaratzoglou, ioannisarapakis, joemonm.jose, Xiangnan He (2019). A Simple Convolutional Generative Network for Next-item Recommendation. In WSDM 2019.

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liziliao, yunshanma, Xiangnan He, richanghong, tat-sengchua (2018). Knowledge-aware Multimodal Dialog Systems. In MM 2018 Best Paper Final List.

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Xiangnan He, zhankuihe, jingkuansong, zhenguangliu, yu-gangjiang, tat-sengchua (2018). NAIS: Neural Attentive Item Similarity Model for Recommendation. In IEEE Transactions on Knowledge and Data Engineering.

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liziliao, Xiangnan He, hanwangzhang, tat-sengchua (2018). Attributed Social Network Embedding. In IEEE Transactions on Knowledge and Data Engineering.

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wenqianglei, xisenjin, min-yenkan, zhaochunren, Xiangnan He (2018). Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures. In ACL 2018.

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Xiangnan He, zhankuihe, xiaoyudu, tat-sengchua (2018). Adversarial Personalized Ranking for Recommendation. In SIGIR 2018.

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Xiang Wang, Xiangnan He, Fuli Feng, liqiangnie, tat-sengchua (2018). TEM: Tree-enhanced Embedding Model for Explainable Recommendation. In WWW 2018.

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Fuli Feng, Xiangnan He, yiqunliu, liqiangnie, tat-sengchua (2018). Learning on Partial-Order Hypergraphs. In WWW 2018.

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jingtaoding, Fuli Feng, Xiangnan He, guanghuiyu, yongli, depengjin (2018). An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data. In WWW 2018 Best Poster Award.

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wenhuiyu, huidizhang, Xiangnan He, xuchen, lixiong, zhengqin (2018). Aesthetic-based Clothing Recommendation. In WWW 2018 Best Paper Award Honorable Mention.

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Xiangnan He, tat-sengchua (2017). Neural Factorization Machines for Sparse Predictive Analytics. In SIGIR 2017.

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Xiang Wang, Xiangnan He, liqiangnie, tat-sengchua (2017). Item Silk Road: Recommending Items from Information Domains to Social Users. In SIGIR 2017.

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jingyuanchen, hanwangzhang, Xiangnan He, liqiangnie, Wei Liu, tat-sengchua (2017). Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-level Attention. In SIGIR 2017.

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Xiangnan He, minggao, min-yenkan, dingxianwang (2017). BiRank: Towards Ranking on Bipartite Graphs. In IEEE Transactions on Knowledge and Data Engineering.

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junxiao, haoye, Xiangnan He, hanwangzhang, feiwu, tat-sengchua (2017). Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. In IJCAI 2017.

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Xiangnan He, liziliao, hanwangzhang, liqiangnie, xiahu, tat-sengchua (2017). Neural Collaborative Filtering. In WWW 2017.

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immanuelbayer, Xiangnan He, bhargavkanagal, steffenrendle (2017). A Generic Coordinate Descent Framework for Learning from Implicit Feedback. In WWW 2017.

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taochen, Xiangnan He, min-yenkan (2016). Context-aware Image Tweets Modelling and Recommendation. In MM 2016..

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Xiangnan He, hanwangzhang, min-yenkan, tat-sengchua (2016). Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In SIGIR 2016..

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hanwangzhang, fuminshen, Wei Liu, Xiangnan He, huanboluan, chuatat-seng (2016). Discrete Collaborative Filtering. In SIGIR 2016 Best Paper Award Honorable Mention.

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Xiangnan He, taochen, min-yenkan, xiaochen (2015). TriRank: Review-aware Explainable Recommendation by Modeling Aspects. In CIKM 2015..

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taochen, hanym.salaheldeen, Xiangnan He, min-yenkan, dongyuanlu (2015). Relating an Image Tweet’s Text and Images. In AAAI 2015..

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Xiangnan He, minggao, min-yenkan, yiqunliu, kazunarisugiyama (2014). Predicting the Popularity of Web 2.0 Items Based on User Comments. In SIGIR 2014.

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Xiangnan He, min-yenkan, peichuxie, xiaochen (2014). Comment-based Multi-View Clustering of Web 2.0 Items. In WWW 2014..

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