RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. - Wayne Xin Zhao; Shanlei Mu; Yupeng Hou; Zihan Lin; Yushuo Chen; Xingyu Pan; Kaiyuan Li; Yujie Lu; Hui Wang 072; Changxin Tian; Yingqian Min; Zhichao Feng; et al. (CIKM, 2021)

Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data. - Hengtong Zhang; Changxin Tian; Yaliang Li; Lu Su 001; Nan Yang; Wayne Xin Zhao; Jing Gao 004 (KDD, 2021)

Multimodal Meta-Learning for Cold-Start Sequential Recommendation. - Xingyu Pan; Yushuo Chen; Changxin Tian; Zihan Lin; Jinpeng Wang 001; He Hu 001; Wayne Xin Zhao (CIKM, 2022)

Temporal Contrastive Pre-Training for Sequential Recommendation. - Changxin Tian; Zihan Lin; Shuqing Bian; Jinpeng Wang 001; Wayne Xin Zhao (CIKM, 2022)

RecBole 2.0: Towards a More Up-to-Date Recommendation Library. - Wayne Xin Zhao; Yupeng Hou; Xingyu Pan; Chen Yang; Zeyu Zhang 007; Zihan Lin; Jingsen Zhang; Shuqing Bian; Jiakai Tang; Wenqi Sun; Yushuo Chen; Lanling Xu; et al. (CIKM, 2022)

Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering. - Changxin Tian; Yuexiang Xie; Yaliang Li; Nan Yang; Wayne Xin Zhao (SIGIR, 2022)

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. - Zihan Lin; Changxin Tian; Yupeng Hou; Wayne Xin Zhao (WWW, 2022)

Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation. - Changxin Tian; Binbin Hu; Wayne Xin Zhao; Zhiqiang Zhang 012; Jun Zhou 011 (CIKM, 2023)

ReLand: Integrating Large Language Models' Insights into Industrial Recommenders via a Controllable Reasoning Pool. - Changxin Tian; Binbin Hu; Chunjing Gan; Haoyu Chen; Zhuo Zhang; Li Yu; Ziqi Liu; Zhiqiang Zhang 012; Jun Zhou 011; Jiawei Chen 007 (RecSys, 2024)

Can Small Language Models be Good Reasoners for Sequential Recommendation? - Yuling Wang; Changxin Tian; Binbin Hu; Yanhua Yu; Ziqi Liu; Zhiqiang Zhang 012; Jun Zhou 011; Liang Pang; Xiao Wang 017 (WWW, 2024)

Privacy-preserving Cross-domain Recommendation with Federated Graph Learning. - Changxin Tian; Yuexiang Xie; Xu Chen 017; Yaliang Li; Xin Zhao 018 (ACM Trans. Inf. Syst., 2024)

BOSE: A Systematic Evaluation Method Optimized for Base Models. - Hongzhi Luan; Changxin Tian; Zhaoxin Huan; Xiaolu Zhang; Kunlong Chen; Zhiqiang Zhang; Jun Zhou (ACL (Findings), 2025)

Toward Stable and Consistent Evaluation Results: A New Methodology for Base Model Evaluation. - Hongzhi Luan; Changxin Tian; Zhaoxin Huan; Xiaolu Zhang; Kunlong Chen; Zhiqiang Zhang 012; Jun Zhou 011 (CoRR, 2025)

Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs. - Ling Team; Binwei Zeng; Chao Huang; Chao Zhang; Changxin Tian; Cong Chen; Dingnan Jin; Feng Yu; Feng Zhu; Feng Yuan; Fakang Wang; Gangshan Wang; et al. (CoRR, 2025)

WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training. - Changxin Tian; Jiapeng Wang; Qian Zhao; Kunlong Chen; Jia Liu; Ziqi Liu; Jiaxin Mao; Wayne Xin Zhao; Zhiqiang Zhang 012; Jun Zhou 011 (CoRR, 2025)

Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models. - Changxin Tian; Kunlong Chen; Jia Liu; Ziqi Liu; Zhiqiang Zhang 012; Jun Zhou 011 (CoRR, 2025)