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WSDM 2024接收的论文已经公布，以下整理了一些LLM和推荐系统相关的文章，全部收录的论文可前往地址
https://www.wsdm-conference.org/2024/accepted-papers/
Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions（阿姆斯特丹，港中文）【让LLM对话：通过零样本LLM-LLM交互模拟人与人的对话QA】
Zahra Abbasiantaeb (University of Amsterdam)*; Yifei Yuan (The Chinese University of Hong Kong); Evangelos Kanoulas (University of Amsterdam); Mohammad Aliannejadi (University of Amsterdam)
GPT4Table: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study（新加坡国立，微软）【GPT4Table:大型语言模型能理解结构化表数据吗？基准与实证研究】
Yuan Sui (National University of Singapore)*; Mengyu Zhou (Microsoft Research); Mingjie Zhou (The University of Hong Kong); Shi Han (Microsoft Research); Dongmei Zhang (Microsoft Research Asia)
Large Language Models for Data Aumgnetation in Recommendation（港大，百度）【LLm为推荐模型做数据增广】
Wei Wei (University of Hong Kong)*; Xubin Ren (the University of Hong Kong); Jiabin Tang (University of Hong Kong); Qinyong Wang (Baidu Inc); Lixin Su (University of Chinese Academy of Sciences); Suqi Cheng (Baidu Inc.); junfeng wang (Baidu); Dawei Yin (Baidu); Chao Huang (University of Hong Kong)
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models（香港理工）【ONCE：用开源和闭源大型语言模型促进基于内容的推荐】 Qijiong Liu (The Hong Kong Polytechnic University)*; NUO CHEN (Waseda University); Tetsuya Sakai (Waseda University); Xiao-Ming Wu (PolyU Hong Kong)
Temporal Blind Spots in Large Language Models【大型语言模型中的时间盲点】
Jonas Wallat (L3S Research Center)*; Adam Jatowt (University of Innsbruck); Avishek Anand (TU Delft)
Defense Against Model Extraction Attacks on Recommender Systems（南阳理工）【推荐系统攻防】 Sixiao Zhang (Nanyang Technological University)*; Hongzhi Yin (The University of Queensland); Hongxu Chen (The University of Queensland); Cheng Long (Nanyang Technological University)
Motif-based Prompt Learning for Universal Cross-domain Recommendation（首都师范）【基于Motif的通用跨域推荐提示学习】
Bowen Hao (Captial Normal University)*; Chaoqun Yang (Griffith University); Lei Guo (Shandong Normal University); Junliang Yu (The University of Queesland); Hongzhi Yin (The University of Queensland)
To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders（亚马逊）【复制或不复制；这是神经序列推荐器中输出Softmax层的一个关键问题】
Haw-shiuan Chang (Amazon)*; Nikhil Agarwal (Amazon.com); Andrew McCallum (Univ of Massachusetts Amherst)
Linear Recurrent Units for Sequential Recommendation（伊利诺伊）【序列推荐的线性递归单元】
Zhenrui Yue (University of Illinois Urbana-Champaign); Yueqi Wang (University of California, Berkeley); Zhankui He (UC, San Diego)*; Huimin Zeng (University of Illinois at Urbana-Champaign); Julian McAuley (UCSD); Dong Wang (University of Illinois Urbana-Champaign)
User Behavior Enriched Temporal Knowledge Graph for Sequential Recommendation（新加坡国立，华为）【用户行为丰富知识图谱，用于序列推荐】
Hengchang Hu (National University of Singapore)*; Wei Guo (Huawei Noah’s Ark Lab); Xu Liu (National University of Singapore); Yong Liu (Huawei); Ruiming Tang (Huawei Noah’s Ark Lab); Rui Zhang (ruizhang.info); Min-Yen Kan (National University of Singapore)
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation（东吴大学）【基于跨子序列的意图对比学习序列推荐】
Xiuyuan Qin (Soochow University)*; Huanhuan Yuan (Soochow University); Pengpeng Zhao (Soochow University); Guanfeng Liu (Macquarie University); Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University); Victor S. Sheng (Texas Tech University)
Budgeted Embedding Table For Recommender Systems（昆士兰）【推荐系统的嵌入表研究】
Yunke Qu (The University of Queensland)*; Tong Chen (The University of Queensland); Quoc Viet Hung Nguyen (Griffith University); Hongzhi Yin (The University of Queensland)
Pre-trained Recommender Systems: A Causal Debiasing Perspective（威斯康星，亚马逊）【预训练推荐系统：因果去偏的视角】
Ziqian Lin (University of Wisconsin–Madison)*; Hao Ding (AWS AI Lab); Nghia Trong Hoang (Washington State University); Branislav Kveton (AWS AI Labs); Anoop Deoras (Amazon); Hao Wang (Rutgers University)
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation（中科大）【动态稀疏学习：一种高效推荐的新范式】
Shuyao Wang (University of Science and Technology of China)*; Yongduo Sui (University of Science and Technology of China); Jiancan Wu (University of Science and Technology of China); Zhi Zheng (University of Science and Technology of China); Hui Xiong (Hong Kong University of Science and Tech)
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation（蚂蚁）【PEACE：用于跨域推荐的原型lEarning增强可迁移框架】
Chunjing Gan (Ant Group)*; Bo Huang (Ant Group); Binbin Hu (Ant Group); Jian Ma (Ant Group); Zhiqiang Zhang (Ant Group); Jun Zhou (Ant Financial); Guannan Zhang (Ant Group); WENLIANG ZHONG (Ant Group)
MADM: A Model-agnostic Denoising Module for Graph-based Social Recommendation（上交）【MADM：一个基于图的社交推荐的模型无关去噪模块】
Wenze Ma (Shanghai Jiao Tong University)*; Yuexian Wang (Shanghai Jiao Tong University); Yanmin Zhu (Shanghai Jiao Tong University); Zhaobo Wang (Shanghai Jiao Tong University); Mengyuan Jing (Shanghai Jiao Tong University); Xuhao Zhao (Shanghai Jiao Tong University); Jiadi Yu (Shanghai Jiao Tong University); Feilong Tang (Shanghai Jiao Tong University)
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation（蒙特利尔，快手）【协作与转换：提取项目转换为序列推荐的多查询自注意力机制】
Tianyu Zhu (University of Montreal)*; Yansong Shi (Tsinghua University); Yuan Zhang (Kuaishou Inc.); Yihong Wu (Université de Montréal); Fengran Mo (Université de Montréal); Jian-Yun Nie (Université de Montréal)
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process（中科院）【CDRNP：通过神经过程向冷启动用户提供跨领域推荐】
Xiaodong Li (Institute of Information Engineering, Chinese Academy of Sciences)*; Jiawei Sheng ( Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China); Jiangxia Cao (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China); Tingwen Liu (Institute of Information Engineering, CAS); Wenyuan Zhang (Institute of Information Engineering, Chinese Academy of Sciences); Quangang Li (Institute of Information Engineering, CAS)
Inverse Learning with Extremely Sparse Feedback for Recommendation（卡耐基梅隆，快手）【具有极稀疏反馈的反向学习推荐】
Guanyu Lin (Carnegie Mellon University)*; Chen Gao (Tsinghua University); Yu Zheng (Tsinghua University); Yinfeng Li (Kuaishou Inc); Jianxin Chang (Kuaishou Inc); Yanan Niu (Kuaishou Inc); Yang Song (Kuaishou Technology); Kun Gai (AI); Zhiheng Li (Tsinghua University); Depeng Jin (Tsinghua University); Yong Li (Tsinghua University)
Contextual MAB Oriented Embedding Denoising for Sequential Recommendation（北邮）【面向上下文MAB的序列推荐嵌入去噪】
Zhichao Feng (Beijing University of Post and Telecommunications); Pengfei Wang (School of Computer Science, Beijing University of Posts and Telecommunications)*; Kaiyuan Li (Beijing University of Posts and Telecommunications); Chenliang Li (Wuhan University); Shangguang Wang (State Key Laboratory of Networking and Switching Technology)
Mixed Attention Network for Cross-domain Sequential Recommendation（卡耐基梅隆，快手）【跨域序列推荐的混合注意网络】
Guanyu Lin (Carnegie Mellon University)*; Chen Gao (Tsinghua University); Yu Zheng (Tsinghua University); Jianxin Chang (Kuaishou Inc); Yanan Niu (Kuaishou Inc); Yang Song (Kuaishou Technology); Kun Gai (AI); Zhiheng Li (Tsinghua University ); Depeng Jin (Tsinghua University); Yong Li (Tsinghua University); Meng Wang (Institute of Artificial Intelligence, Hefei Comprehensive National Science Center)
Knowledge Graph Context-Enhanced Diversified Recommendation（伊利诺伊）【知识图谱上下文增强的多样化推荐】
Xiaolong Liu (University of Illinois at Chicago)*; Liangwei Yang (University of Illinois at Chicago); Zhiwei Liu (Salesforce); Mingdai Yang (University of Illinios at Chicago); Chen Wang (University of Illinois at Chicago); Hao Peng (Beihang University); Philip S Yu (UIC)
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights（西湖大学）【基于适配器的推荐系统迁移学习探索：实证研究与实践启示】
Junchen Fu (Westlake University)*; Fajie Yuan (Westlake University); Yu Song (Westlake University); Zheng Yuan (Westlake University); Mingyue Cheng (University of Science and Technology of China); Shenghui Cheng (Westlake University); Jiaqi Zhang (Westlake University); Jie Wang (Westlake University); Yunzhu Pan (University of Electronic Science and Technology of China)
Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation（香港城市大学，华为）【Diff-MSR：冷启动多场景推荐的扩散模型增强范式】
Yuhao Wang (City University of Hong Kong)*; Ziru Liu (City University Of HongKong ); Yichao Wang (Huawei Noah’s Ark Lab); Xiangyu Zhao (City University of Hong Kong); Bo Chen (Huawei Noah’s Ark Lab); Huifeng Guo (Huawei Noah’s Ark Lab); Ruiming Tang (Huawei Noah’s Ark Lab)
AutoPooling: Automated Pooling Search for Multi-valued Features in Recommendations（腾讯）
He Wei (Tencent Inc.)*; Meixi Liu (Machine learning platform department, Tencent TEG); Yang Zhang (Tencent Inc)
C^2DR: Robust Cross-Domain Recommendation based on Causal Disentanglement（中南）【C^2DR：基于因果解耦的鲁棒跨域推荐】
Menglin Kong (Central South University); Jia Wang (Xi’an Jiaotong-Liverpool University)*; Yushan Pan (Xi’an Jiaotong-Liverpool University); Haiyang Zhang (Xi’an Jiaotong-Liverpool University); Muzhou Hou (Central South Uinversity)
Unified Pretraining for Recommendation via Task Hypergraphs（伊利诺伊，Salesforce）【基于任务超图的推荐统一预训练】
Mingdai Yang (University of Illinios at Chicago)*; Zhiwei Liu (Salesforce); Liangwei Yang (University of Illinois at Chicago); Xiaolong Liu (University of Illinois at Chicago); Chen Wang (University of Illinois at Chicago); Hao Peng (Beihang University); Philip S Yu (UIC)
SSLRec: A Self-Supervised Learning Library for Recommendation（港大）【自监督推荐库】
Xubin Ren (the University of Hong Kong)*; Lianghao Xia (University of Hong Kong); Yuhao Yang (Wuhan University); Wei Wei (University of Hong Kong); Tianle Wang (HKU); Xuheng Cai (The University of Hong Kong); Chao Huang (University of Hong Kong)
Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation（深圳大学，腾讯）【辅助信息集成序列推荐的多序列注意用户表征学习】
Xiaolin Lin (Shenzhen University)*; Jinwei Luo (Shenzhen University); Junwei Pan (Tencent); Weike Pan (Shenzhen University); Zhong Ming (Shenzhen University); Xun Liu (Tencent); HUANG SHUDONG (tencent); Jie Jiang (Tencent Inc.)
LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting（中科大，快手）【LabelCraft：通过自动标签制作实现短视频推荐】
Yimeng Bai (University of Science and Technology of China)*; Yang Zhang (University of Science and Technology of China); Jing Lu (Kuaishou Inc); Jianxin Chang (Kuaishou Inc); Xiaoxue Zang (Kuaishou Inc); Yanan Niu (Kuaishou); Yang Song (Kuaishou Technology); Fuli Feng (University of Science and Technology of China)
MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation（汉阳大学）【MONET：包含图卷积网络的模态和多媒体推荐的目标感知注意力】
Yungi Kim (Hanyang University); Taeri Kim (Hanyang University); Won-Yong Shin (Yonsei University, Korea); Sang-Wook Kim (Hanyang University, Korea)*
RecJPQ: Training Large-Catalogue Sequential Recommenders【RecJPQ：训练大型目录序列推荐】
Aleksandr V Petrov (University of Glasgow)*; Craig Macdonald (University of Glasgow)
On the Effectiveness of Unlearning in Session-Based Recommendation（山大）【基于会话的推荐中释放的有效性研究】
Xin Xin (Shandong University); Liu Yang (Shandong University)*; Ziqi Zhao (Shandong University); Pengjie Ren (Shandong University); Zhumin Chen (Shandong University); Jun Ma (Shandong University); Zhaochun Ren (Leiden University)
Proxy-based Item Representation for Attribute and Context-aware Recommendation（首尔国立大学）【基于代理的item表征】
Jinseok Seol (Seoul National University)*; Minseok Gang (Seoul National University); Sang-goo Lee (Seoul National University); Jaehui Park (University of Seoul)
IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation（清华，华为）【IncMSR：一种用于多场景推荐的增量学习方法】
Kexin Zhang (Tsinghua University)*; Yichao Wang (Huawei Noah’s Ark Lab); Xiu Li (Tsinghua University); Ruiming Tang (Huawei Noah’s Ark Lab); Rui Zhang (ruizhang.info)
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation（阿里）【触发推荐中CTR预测的深度进化即时兴趣网络】
Zhibo Xiao (Alibaba Group)*; Luwei Yang (Alibaba Group); Tao Zhang (Alibaba Group); Wen Jiang (Alibaba Group); Wei Ning ( Alibaba Group); Yujiu Yang (Tsinghua University)
User Consented Federated Recommender System Against Personalized Attribute Inference Attack
Qi Hu (Hong Kong University of Science and Technology)*; Yangqiu Song (Hong Kong University of Science and Technology)
Neural Kalman Filtering for Robust Temporal Recommendation（复旦，微软，亚马逊）【用于鲁棒时间推荐的神经卡尔曼滤波】
Jiafeng Xia (Fudan University)*; Dongsheng Li (Microsoft Research Asia); Hansu Gu (Amazon.com); Tun Lu (Fudan University); Peng Zhang (Fudan University); Li Shang (Fudan University); Ning Gu (Fudan University)
Attribute Simulation for Item Embedding Enhancement in Multi-interest Recommendation（天大）【多兴趣推荐中项目嵌入增强的属性仿真】
Yaokun Liu (Tianjin University)*; Xiaowang Zhang (Tianjin University); Minghui Zou (Tianjin University); Zhiyong Feng (Tianjin University)
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure（山大）【基于系统曝光的分布鲁棒优化对序列推荐去偏】
Jiyuan Yang (Shandong University)*; Yue Ding (Shanghai Jiao Tong University); YIDAN WANG (SHANDONG UNIVERSITY); Pengjie Ren (Shandong University); Zhumin Chen (Shandong University); Fei Cai (National University of Defense Technology); Jun Ma (Shandong University); Rui Zhang (ruizhang.info); Zhaochun Ren (Leiden University); Xin Xin (Shandong University)
Knowledge Graph Diffusion Model for Recommendation（港大）【知识图扩散模型用于推荐】
Yangqin Jiang (University of Hong Kong)*; Yuhao Yang (Wuhan University); Lianghao Xia (University of Hong Kong); Chao Huang (University of Hong Kong)
Interact with the Explanations: Causal Debiased Explainable Recommendation System（上交，adobe）【因果去偏可解释推荐系统】
Xu Liu (Shanghai Jiao Tong University); Tong Yu (Adobe Research); Kaige Xie (Georgia Institute of Technology); Junda Wu (New York University); Shuai Li (Shanghai Jiao Tong University)*
Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation（天大）【多行为序列推荐的全局异构图和目标兴趣去噪】
Xuewei Li (Tianjin University); Hongwei Chen (College of Intelligence and Computing, Tianjin University)*; Jian Yu (Tianjin University); Mankun Zhao (Tianjin University); Tianyi Xu (Tianjin University); Wenbin Zhang (Information and Network Center, Tianjin University); Mei Yu (Tianjin University)
MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems【MultiFS:深度推荐系统中的自动多场景特征选择】
Dugang Liu (Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University)*; Chaohua Yang (Shenzhen University); Xing Tang (Tencent); Yejing Wang (City University of Hongkong); Fuyuan Lyu (McGill University); weihong luo (tencent); Xiuqiang He (Tencent); Zhong Ming (Shenzhen University); Xiangyu Zhao (City University of Hong Kong)
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction（山大，阿里）【list-wise蒸馏用于CTR预测校准】
Xiaoqiang Gui (Shandong University)*; Yueyao Cheng (Alibaba Group); Xiang-Rong Sheng (Alibaba Group); Yunfeng Zhao (Shandong University); Guoxian Yu (Shandong University); Shuguang Han (Alibaba Inc.); Yuning Jiang (Alibaba Group); Jian Xu (Alibaba Group); Bo Zheng (Alibaba Group)
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