| 2023 | Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation. | Yichi Zhang, Guisheng Yin, Yuxin Dong |
| 2023 | Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. | Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He |
| 2023 | User-Centric Conversational Recommendation: Adapting the Need of User with Large Language Models. | Gangyi Zhang |
| 2023 | Learning the True Objectives of Multiple Tasks in Sequential Behavior Modeling. | Jiawei Zhang |
| 2023 | Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation. | Meng Yuan, Fuzhen Zhuang, Zhao Zhang, Deqing Wang, Jin Dong |
| 2023 | Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application. | Jianjun Yuan, Wei Lee Woon, Ludovik Coba |
| 2023 | Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized Recommendation. | Congrui Yi, David Zumwalt, Zijian Ni, Shreya Chakrabarti |
| 2023 | Online Matching: A Real-time Bandit System for Large-scale Recommendations. | Xinyang Yi, Shao-Chuan Wang, Ruining He, Hariharan Chandrasekaran, Charles Wu, Lukasz Heldt, Lichan Hong, Minmin Chen, Ed H. Chi |
| 2023 | Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM. | Bin Yin, Junjie Xie, Yu Qin, Zixiang Ding, Zhichao Feng, Xiang Li, Wei Lin |
| 2023 | DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender. | Xiaoxin Ye, Yun Li, Lina Yao |
| 2023 | Towards Robust Fairness-aware Recommendation. | Hao Yang, Zhining Liu, Zeyu Zhang, Chenyi Zhuang, Xu Chen |
| 2023 | ✨ Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations. | Boming Yang, Dairui Liu, Toyotaro Suzumura, Ruihai Dong, Irene Li |
| 2023 | A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation. | Zitao Xu, Weike Pan, Zhong Ming |
| 2023 | Graph Enhanced Feature Engineering for Privacy Preserving Recommendation Systems. | Chendi Xue, Xinyao Wang, Yu Zhou, Poovaiah M. Palangappa, Rita Brugarolas Brufau, Aasavari Dhananjay Kakne, Ravi Motwani, Ke Ding, Jian Zhang |
| 2023 | Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning. | Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych, Fan Liu, Yuchen He, Alex Nikulkov, Zheqing Zhu |
| 2023 | Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation. | Xumei Xi, Yuke Zhao, Quan Liu, Liwen Ouyang, Yang Wu |
| 2023 | Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems. | Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jae Boum Kim, Fangzhao Wu, Sunghun Kim |
| 2023 | Customer Lifetime Value Prediction: Towards the Paradigm Shift of Recommender System Objectives. | Chuhan Wu, Qinglin Jia, Zhenhua Dong, Ruiming Tang |
| 2023 | Enhanced Privacy Preservation for Recommender Systems. | Ziqing Wu |
| 2023 | Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions. | Timo Wilm, Philipp Normann, Sophie Baumeister, Paul-Vincent Kobow |
| 2023 | Multi-Relational Contrastive Learning for Recommendation. | Wei Wei, Lianghao Xia, Chao Huang |
| 2023 | The Effect of Random Seeds for Data Splitting on Recommendation Accuracy. | Lukas Wegmeth, Tobias Vente, Lennart Purucker, Joeran Beel |
| 2023 | Improving Recommender Systems Through the Automation of Design Decisions. | Lukas Wegmeth |
| 2023 | An Industrial Framework for Personalized Serendipitous Recommendation in E-commerce. | Zongyi Wang, Yanyan Zou, Anyu Dai, Linfang Hou, Nan Qiao, Luobao Zou, Mian Ma, Zhuoye Ding, Sulong Xu |
| 2023 | RecAD: Towards A Unified Library for Recommender Attack and Defense. | Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, Xiangnan He |