SIGIR 2022 推荐系统相关论文整理分类
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SIGIR 2022论文接收情况已出，本文为大家整理了推荐系统相关的论文分别按照应用和热门技术进行了划分，包括序列推荐，会话推荐，点击率预估，图学习，automl，对比学习等。
来源：https://sigir.org/sigir2022/program/accepted/
广告分配问题
来自美团外卖广告算法团队的论文：Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation
【广告分配中的页面级兴趣建模】Guogang Liao, Xiaowen Shi, Ze Wang, Xiaoxu Wu, Chuheng Zhang, Yongkang Wang, Xingxing Wang and Dong Wang
冷启动 cold start
Socially-aware Dual Contrastive Learning for Cold-Start Recommendation【冷启动推荐的社会意识双重对比学习】 Jing Du, Zesheng Ye, Lina Yao, Bin Guo and Zhiwen Yu
Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation【通过大规模推荐中的融合行为将冷启动用户转变为温暖用户】 Pengyang Li, Rong Chen, Quan Liu, Jian Xu and Bo Zheng
Generative Adversarial Framework for Cold-Start Item Recommendation【冷启动项目推荐的生成对抗框架】 Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He and Zhoujun Li
Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder【通过与模型无关的条件变分自动编码器改进项目冷启动推荐】 Xu Zhao, Yi Ren, Ying Du, Shenzheng Zhang and Nian Wang
协同过滤 collaborative filtering
Geometric Disentangled Collaborative Filtering 【几何解耦的协同过滤】Yiding Zhang, Chaozhuo Li, Xing Xie, Xiao Wang, Chuan Shi, Yuming Liu, Hao Sun, Liangjie Zhang, Weiwei Deng and Qi Zhang
Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering 【超图上的对比学习】Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin and Jimmy Huang
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering 【图协同过滤在准确度和新颖度上的表现】Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Runze Wu, Xudong Shen and Tangjie Lv
Enhancing Top-N Item Recommendations by Peer Collaboration 【同龄人协同】Yang Sun, Fajie Yuan, Min Yang, Alexandros Karatzoglou, Li Shen and Xiaoyan Zhao
Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering 【Chenglong Ma, Yongli Ren, Pablo Castells and Mark Sanderson】
序列推荐 sequential recommendation
Decoupled Side Information Fusion for Sequential Recommendation【解耦边缘信息的序列推荐】 Yueqi Xie, Peilin Zhou and Sunghun Kim
Multi-Agent RL-based Information Selection Model for Sequential Recommendation【基于多智能体 RL 的信息选择模型】 Kaiyuan Li, Pengfei Wang and Chenliang Li
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation【当多层次遇到多利益时：用于序列推荐的多粒度神经模型】 Yu Tian, Jianxin Chang, Yanan Niu, Yang Song and Chenliang Li
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation【Ada-Ranker：一种用于序列推荐的数据分布自适应排序范式】 Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li and Xing Xie
Multi-Behavior Sequential Transformer Recommender【多行为序列 Transformer 推荐方法】 Enming Yuan, Wei Guo, Zhicheng He, Huifeng Guo, Chengkai Liu and Ruiming Tang
Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation【DPP损失函数】 Yuli Liu, Christian Walder and Lexing Xie
Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation【用于序列推荐的具有不对称位置编码的渐进式自注意力网络】 Yuehua Zhu, Bo Huang, Shaohua Jiang, Muli Yang, Yanhua Yang and Wenliang Zhong
RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation【RESETBERT4Rec：一个整合时间和用户历史行为的序列推荐的预训练模型】 Qihang Zhao
Item-Provider Co-learning for Sequential Recommendation【序列推荐，商品-提供者协同学习】 Lei Chen, Jingtao Ding, Min Yang, Chengming Li, Chonggang Song and Lingling Yi
Improving Conversational Recommender Systems via Transformer-based Sequential Modelling【通过基于 Transformer 的序列建模改进对话推荐系统】 Jie Zou, Evangelos Kanoulas, Pengjie Ren, Zhaochun Ren, Aixin Sun and Cheng Long
Is News Recommendation a Sequential Recommendation Task?【新闻推荐是顺序推荐任务吗？】 Chuhan Wu, Fangzhao Wu, Tao Qi, Chenliang Li and Yongfeng Huang
Coarse-to-Fine Sparse Sequential Recommendation【粗到细的稀疏序列推荐】 Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo-Min Pantel and Julian McAuley
ELECRec: Training Sequential Recommenders as Discriminators【ELECRec：将序列推荐训练为鉴别器】 Yongjun Chen, Jia Li and Caiming Xiong
Exploiting Session Information in BERT-based Session-aware Sequential Recommendation【在基于 BERT 的会话感知顺序推荐中利用会话信息】 Jinseok Seol, Youngrok Ko and Sang-Goo Lee
Dual Contrastive Network for Sequential Recommendation【用于序列推荐的双对比网络】 Guanyu Lin, Chen Gao, Yinfeng Li, Yu Zheng, Zhiheng Li, Depeng Jin and Yong Li
会话推荐 session recommendation
An Attribute-Driven Mirroring Graph Network for Session-based Recommendation【基于会话推荐的属性驱动镜像图网络】 Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang and Aixin Sun
Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation【基于会话的推荐中的价格和兴趣偏好建模】 Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu and Hongfei Lin
AutoGSR: Neural Architecture Search for Graph-based Session Recommendation【AutoGSR：基于图的会话推荐的神经架构搜索】 Jingfan Chen, Guanghui Zhu, Haojun Hou, Chunfeng Yuan and Yihua Huang
Multi-Faceted Global Item Relation Learning for Session-Based Recommendation【基于会话的推荐的多方面全局商品关系学习】 Qilong Han, Chi Zhang, Rui Chen, Riwei Lai, Hongtao Song and Li Li
Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation【正面、负面和中立：在基于会话的新闻推荐中建模隐式反馈】 Shansan Gong and Kenny Zhu
Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation【通过意图解耦来增强超图神经网络】 Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin and Yong Li
DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation【DAGNN：用于基于会话推荐的需求感知图神经网络】 Liqi Yang, Linhao Luo, Xiaofeng Zhang, Fengxin Li, Xinni Zhang, Zelin Jiang and Shuai Tang
CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space【表征空间一致性】 Yupeng Hou, Binbin Hu, Zhiqiang Zhang and Wayne Xin Zhao
Explainable Session-based Recommendation with Meta-Path Guided Instances and Self-Attention Mechanism【具有元路径引导实例和自注意机制的可解释的会话推荐】 Jiayin Zheng, Juanyun Mai and Yanlong Wen
点击率预估 ctr
Enhancing CTR Prediction with Context-Aware Feature Representation Learning 【上下文相关的特征表示】Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang and Ning Gu
HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction 【层次化意图嵌入网络】Zuowu Zheng, Changwang Zhang, Xiaofeng Gao and Guihai Chen
NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction 【高效的网络结构搜索】Guanghui Zhu, Feng Cheng, Defu Lian, Chunfeng Yuan and Yihua Huang
INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering 【模型无关的归纳式协同过滤模块】Yunfan Wu, Qi Cao, Huawei Shen, Shuchang Tao and Xueqi Cheng
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer 【图遮盖的Transformer】Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Luo Da, Kangyi Lin, Sophia Ananiadou and Junzhou Huang
Neural Statistics for Click-Through Rate Prediction 【神经统计学】Yanhua Huang, Hangyu Wang, Yiyun Miao, Ruiwen Xu, Lei Zhang and Weinan Zhang
Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction 【基于排序的CTR预估】Shuang Tang, Fangyuan Luo, Jun Wu and Zhuo Wang
DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction 【基于图的解耦表示】Yifan Wang, Yifang Qin, Fang Sun, Bo Zhang, Xuyang Hou, Ke Hu, Jia Cheng, Jun Lei and Ming Zhang
Deep Multi-Representational Item Network for CTR Prediction 【多重表示商品网络】Jihai Zhang, Fangquan Lin, Cheng Yang and Wei Wang
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction 【多分辨率小波分析】Xiaoxiao Xu, Zhiwei Fang, Qian Yu, Ruoran Huang, Chaosheng Fan, Yong Li, Yang He, Changping Peng, Zhangang Lin and Jingping Shao
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios 【小规模推荐场景下的元学习】Xiaofeng Pan, Ming Li, Jing Zhang, Keren Yu, Luping Wang, Hong Wen, Chengjun Mao and Bo Cao
Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction 【对抗过滤建模用户长期行为序列】Xiaochen Li, Jian Liang, Xialong Liu and Yu Zhang
Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction 【长序列数据集基于聚类的行为采样】Yuren Zhang, Enhong Chen, Binbin Jin, Hao Wang, Min Hou, Wei Huang and Runlong Yu
CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper 【新颖度辅助任务】Dandan Zhang, Haotian Wu, Guanqi Zeng, Yao Yang, Weijiang Qiu, Yujie Chen and Haoyuan Hu
跨域 cross domain
DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation【DisenCDR：学习跨域推荐的解耦表征】 Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu and Bin Wang
Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation【双自适应强化学习】 Junda Wu, Zhihui Xie, Tong Yu, Handong Zhao, Ruiyi Zhang and Shuai Li
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System【多级跨视图对比学习】 Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu and Xin Cao
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation【利用变分域不变用户embedding进行部分重叠的跨域推荐】 Weiming Liu, Xiaolin Zheng, Jiajie Su, Mengling Hu, Yanchao Tan and Chaochao Chen
新闻推荐 news recommendation
ProFairRec: Provider Fairness-aware News Recommendation【ProFairRec：提供者公平感知的新闻推荐】 Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang and Xing Xie
News Recommendation with Candidate-aware User Modeling【具有候选感知用户建模的新闻推荐】 Tao Qi, Fangzhao Wu, Chuhan Wu and Yongfeng Huang
FUM: Fine-grained and Fast User Modeling for News Recommendation【FUM：新闻推荐的细粒度和快速用户建模】 Tao Qi, Fangzhao Wu, Chuhan Wu and Yongfeng Huang
MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation【多模态新闻推荐】 Chuhan Wu, Fangzhao Wu, Tao Qi, Chao Zhang, Yongfeng Huang and Tong Xu
Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation【正面、负面和中立：在基于会话的新闻推荐中建模隐式反馈】 Shansan Gong and Kenny Zhu
对话推荐 conversational recommendation
Learning to Infer User Implicit Preference in Conversational Recommendation【对话学习中用户隐式偏好推断】 Chenhao Hu, Shuhua Huang, Yansen Zhang and Yubao Liu
User-Centric Conversational Recommendation with Multi-Aspect User Modeling【具有多方面用户建模的以用户为中心的会话推荐】 Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang and Qing He
Variational Reasoning about User Preferences for Conversational Recommendation【话推荐的用户偏好的变分推理】 Zhi Tian, Zhaochun Ren, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, Maarten de Rijke and Zhumin Chen
Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems【分析和模拟会话推荐系统中的用户话语重构】 Shuo Zhang, Mu Chun Wang and Krisztian Balog
Conversational Recommendation via Hierarchical Information Modeling【通过分层信息建模的对话推荐】 Quan Tu, Shen Gao, Yanran Li, Jianwei Cui, Bin Wang and Rui Yan
Improving Conversational Recommender Systems via Transformer-based Sequential Modelling【通过基于 Transformer 的序列建模改进对话推荐系统】 Jie Zou, Evangelos Kanoulas, Pengjie Ren, Zhaochun Ren, Aixin Sun and Cheng Long
POI推荐
Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation【分层多任务图RNN，POI推荐】
Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng and Rui Tan
Learning Graph-based Disentangled Representations for Next POI Recommendation【基于图表征解耦】 Zhaobo Wang, Yanmin Zhu, Haobing Liu and Chunyang Wang
GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation【GETNext：用于POI 推荐的轨迹流图增强型Transformer】 Song Yang, Jiamou Liu and Kaiqi Zhao
Empowering Next POI Recommendation with Multi-Relational Modeling【多关系建模】 Zheng Huang, Jing Ma, Yushun Dong, Natasha Foutz and Jundong Li
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning【MP2：基于点和成对学习的推荐的动量对比方法】 Menghan Wang, Yuchen Guo, Zhenqi Zhao, Guangzheng Hu, Yuming Shen, Mingming Gong and Philip Torr
Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network【具有自相关增强型多模态Transformer网络】 Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao and Chenxing Wang
纠偏 debiased recommendation
Interpolative Distillation for Unifying Biased and Debiased Recommendation【统一有偏和去偏推荐的插值蒸馏】
Sihao Ding, Fuli Feng, Xiangnan He, Jinqiu Jin, Wenjie Wang, Yong Liao and Yongdong Zhang
Bilateral Self-unbiased Recommender Learning from Biased Implicit Feedback【从有偏隐式反馈中学习的双边无偏推荐器】 Jae-woong Lee, Seongmin Park, Joonseok Lee and Jongwuk Lee
Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders【协同训练解耦域适应网络以利用推荐器中的流行度偏差】 Zhihong Chen, Jiawei Wu, Chenliang Li, Jingxu Chen, Rong Xiao and Binqiang Zhao
Mitigating Consumer Biases in Recommendations with Adversarial Training【通过对抗训练减轻消费者在推荐中的偏见】 Christian Ganhor, David Penz, Navid Rekabsaz, Oleg Lesota and Markus Schedl
DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation【DeSCoVeR：用于场地推荐的去偏语义上下文先验】 Sailaja Rajanala, Arghya Pal, Manish Singh, Raphael Phan and Wong Koksheik
Neutralizing Popularity Bias in Recommendation Models【流行度偏差】 Guipeng Xv, Chen Lin, Hui Li, Jinsong Su, Weiyao Ye and Yewang Chen
在线推荐 online recommendation
Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation【在线推荐中稀疏奖励的局部敏感状态引导体验重放优化】
Xiaocong Chen, Lina Yao, Julian Mcauley, Weili Guan, Xiaojun Chang and Xianzhi Wang
公平性/多样性/个性化等 fair diversity Personalize
ProFairRec: Provider Fairness-aware News Recommendation【ProFairRec：提供者公平感知的新闻推荐】 Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang and Xing Xie
Joint Multisided Exposure Fairness for Recommendation【公平性推荐，混合曝光】 Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz and Xue Liu
Explainable Fairness for Feature-aware Recommender Systems【特征感知推荐系统的可解释公平性】 Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li and Yongfeng Zhang
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems【CPFair：推荐系统的个性化消费者和生产者公平重新排名】 Mohammadmehdi Naghiaei, Hossein A. Rahmani and Yashar Deldjoo
Selective Fairness in Recommendation via Prompts【选择公平性】 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin and Qing He
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations【具有知识图谱的后处理推荐系统，用于解释的新鲜度、流行度和多样性】 Giacomo Balloccu, Ludovico Boratto, Mirko Marras and Gianni Fenu
DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph【DAWAR：基于相关图的混搭创建的多样性感知 Web API 推荐】 Wenwen Gong, Xuyun Zhang, Yifei Chen, Qiang He, Amin Beheshti, Xiaolong Xu, Chao Yan and Lianyong Qi
Diversity vs Relevance: a practical multi-objective study in luxury fashion recommendations【多样性与相关性：奢侈品时尚推荐的实用多目标研究】 Joao Sa, Vanessa Queiroz Marinho, Ana Rita Magalhaes, Tiago Lacerda and Diogo Goncalves
PEVAE: A hierarchical VAE for personalized explainable recommendation.【PEVAE：用于个性化可解释推荐的分层 VAE】 Zefeng Cai and Zerui Cai
PERD: Personalized Emoji Recommendation with Dynamic User Preference【PERD：具有动态用户偏好的个性化表情符号推荐】 Xuanzhi Zheng, Guoshuai Zhao, Li Zhu and Xueming Qian
图学习 graph
EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems【EFLEC：基于 GNN 的推荐系统中的高效特征校正】 Ishaan Kumar, Yaochen Hu and Yingxue Zhang
Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation【通过意图解耦来增强超图神经网络】 Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin and Yong Li
DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation【DAGNN：用于基于会话推荐的需求感知图神经网络】 Liqi Yang, Linhao Luo, Xiaofeng Zhang, Fengxin Li, Xinni Zhang, Zelin Jiang and Shuai Tang
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer 【图遮盖的Transformer】Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Luo Da, Kangyi Lin, Sophia Ananiadou and Junzhou Huang
DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction 【基于图的解耦表示】Yifan Wang, Yifang Qin, Fang Sun, Bo Zhang, Xuyang Hou, Ke Hu, Jia Cheng, Jun Lei and Ming Zhang
An Attribute-Driven Mirroring Graph Network for Session-based Recommendation【基于会话推荐的属性驱动镜像图网络】 Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang and Aixin Sun
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations【具有知识图谱的后处理推荐系统，用于解释的新鲜度、流行度和多样性】 Giacomo Balloccu, Ludovico Boratto, Mirko Marras and Gianni Fenu
A Review-aware Graph Contrastive Learning Framework for Recommendation【评论感知图对比学习框架】 Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang and Yong Li
Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation【是否需要图增强？用于推荐的简单图对比学习】 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui and Quoc Viet Hung Nguyen
Knowledge Graph Contrastive Learning for Recommendation【知识图谱对比学习】 Yuhao Yang, Chao Huang, Lianghao Xia and Chenliang Li
Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering【超图对比协同过滤】 Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin and Jimmy Huang
MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations【MGPolicy：元图增强的离策略学习推荐】 Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen and Guandong Xu
DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph【DAWAR：基于相关图的混搭创建的多样性感知 Web API 推荐】 Wenwen Gong, Xuyun Zhang, Yifei Chen, Qiang He, Amin Beheshti, Xiaolong Xu, Chao Yan and Lianyong Qi
Graph Trend Filtering Networks for Recommendation【用于推荐的图趋势过滤网络】 Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang and Qing Li
KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums【KETCH：医疗保健论坛中的知识图增强线程推荐】 Limeng Cui and Dongwon Lee
Less is More: Reweighting Important Spectral Graph Features for Recommendation【重加权重要的频谱图特征】 Shaowen Peng, Kazunari Sugiyama and Tsunenori Min
Adversarial Graph Perturbations for Recommendations at Scale【大规模推荐的对抗图扰动】 Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Xia Hu, Fei Wang and Hao Yang
DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations【DH-HGCN：用于多种社交推荐的双同质超图卷积网络】 Jiadi Han, Qian Tao, Yufei Tang and Yuhan Xia
Multi-modal Graph Contrastive Learning for Micro-video Recommendation【微视频推荐的多模态图对比学习】 Zixuan Yi, Xi Wang, Craig Macdonald and Iadh Ounis
An MLP-based Algorithm for Efficient Contrastive Graph Recommendations【一种基于 MLP 的高效对比图推荐算法】 Siwei Liu, Iadh Ounis and Craig Macdonald
Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation【分层多任务图RNN，POI推荐】 Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng and Rui Tan
Learning Graph-based Disentangled Representations for Next POI Recommendation【基于图表征解耦】 Zhaobo Wang, Yanmin Zhu, Haobing Liu and Chunyang Wang
AutoGSR: Neural Architecture Search for Graph-based Session Recommendation【AutoGSR：基于图的会话推荐的神经架构搜索】 Jingfan Chen, Guanghui Zhu, Haojun Hou, Chunfeng Yuan and Yihua Huang
AutoML
AutoLossGen: Automatic Loss Function Generation for Recommender Systems【AutoLossGen：推荐系统的自动损失函数生成】 Zelong Li, Jianchao Ji, Yingqiang Ge and Yongfeng Zhang
AutoGSR: Neural Architecture Search for Graph-based Session Recommendation【AutoGSR：基于图的会话推荐的神经架构搜索】 Jingfan Chen, Guanghui Zhu, Haojun Hou, Chunfeng Yuan and Yihua Huang
Single-shot Embedding Dimension Search in Recommender System【自动embedding维度搜索】 Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi and Hongzhi Yin
NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction 【高效的网络结构搜索】Guanghui Zhu, Feng Cheng, Defu Lian, Chunfeng Yuan and Yihua Huang
多任务学习 multi-task
Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation【分层多任务图RNN，POI推荐】 Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng and Rui Tan
对比学习 contrastive learning
Improving Micro-video Recommendation via Contrastive Multiple Interests【通过对比多兴趣改进微视频推荐】 Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng and Wei Zhuo
Socially-aware Dual Contrastive Learning for Cold-Start Recommendation【冷启动推荐的社会意识双重对比学习】 Jing Du, Zesheng Ye, Lina Yao, Bin Guo and Zhiwen Yu
Multi-modal Graph Contrastive Learning for Micro-video Recommendation【微视频推荐的多模态图对比学习】 Zixuan Yi, Xi Wang, Craig Macdonald and Iadh Ounis
An MLP-based Algorithm for Efficient Contrastive Graph Recommendations【一种基于 MLP 的高效对比图推荐算法】 Siwei Liu, Iadh Ounis and Craig Macdonald
Unify Local and Global Information for Top-N Recommendation【统一局部信息和全局信息】
Xiaoming Liu, Shaocong Wu, Zhaohan Zhang and Chao Shen
Dual Contrastive Network for Sequential Recommendation【用于序列推荐的双对比网络】 Guanyu Lin, Chen Gao, Yinfeng Li, Yu Zheng, Zhiheng Li, Depeng Jin and Yong Li
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System【多级跨视图对比学习】 Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu and Xin Cao
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning【MP2：基于点和成对学习的推荐的动量对比方法】 Menghan Wang, Yuchen Guo, Zhenqi Zhao, Guangzheng Hu, Yuming Shen, Mingming Gong and Philip Torr
A Review-aware Graph Contrastive Learning Framework for Recommendation【评论感知图对比学习框架】 Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang and Yong Li
Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation【是否需要图增强？用于推荐的简单图对比学习】 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui and Quoc Viet Hung Nguyen
Knowledge Graph Contrastive Learning for Recommendation【知识图谱对比学习】 Yuhao Yang, Chao Huang, Lianghao Xia and Chenliang Li
Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering【超图对比协同过滤】 Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin and Jimmy Huang
强化学习 RL
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective【强化学习用于推荐】 Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou and Zhaochun Ren
Revisiting Interactive Recommender System with Reinforcement Learning【用强化学习重新审视交互式推荐系统】 Hojoon Lee, Dongyoon Hwang, Kyushik Min and Jaegul Choo
Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation【双自适应强化学习】 Junda Wu, Zhihui Xie, Tong Yu, Handong Zhao, Ruiyi Zhang and Shuai Li
Value Penalized Q-Learning for Recommender Systems【推荐系统的价值惩罚 Q-learning】 Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan and Peilin Zhao
MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations【MGPolicy：元图增强的离策略学习推荐】 Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen and Guandong Xu
Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation【在线推荐中稀疏奖励的局部敏感状态引导体验重放优化】 Xiaocong Chen, Lina Yao, Julian Mcauley, Weili Guan, Xiaojun Chang and Xianzhi Wang
Multi-Agent RL-based Information Selection Model for Sequential Recommendation【基于多智能体 RL 的信息选择模型】 Kaiyuan Li, Pengfei Wang and Chenliang Li
其他
User-Aware Multi-Interest Learning for Candidate Matching in Recommenders【候选匹配的用户感知多兴趣学习】
Zheng Chai, Zhihong Chen, Chenliang Li, Rong Xiao, Houyi Li, Jiawei Wu, Jingxu Chen and Haihong Tang
User-controllable Recommendation Against Filter Bubbles【用户可控的推荐】
Wenjie Wang, Fuli Feng, Liqiang Nie and Tat-Seng Chua
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation【学习用于群体推荐的超立方体表示】
Tong Chen, Hongzhi Yin, Jing Long, Nguyen Quoc Viet Hung, Yang Wang and Meng Wang
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation【自监督知识蒸馏】
Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu and Quoc Viet Hung Nguyen
Forest-based Deep Recommender【基于森林的推荐】
Chao Feng, Defu Lian, Zheng Liu, Xing Xie, Le Wu and Enhong Chen
Privacy-Preserving Synthetic Data Generation for Recommendation【用于推荐的隐私保护合成数据生成】
Fan Liu, Huilin Chen, Zhiyong Cheng, Yinwei Wei, Liqiang Nie and Mohan Kankanhalli
Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator【通过反事实生成器减轻知识感知推荐中的虚假相关性】
Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding and Ji-Rong Wen
HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation【HAKG：用于推荐的层次结构感知知识门控网络】
Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng and Yunjun Gao
Self-Guided Learning to Denoise for Robust Recommendation【自引导学习去噪以实现鲁棒推荐】
Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang and Baihua Zheng
ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping【ReCANet：一个重复消费感知神经网络，用于杂货店购物中的下一个购物篮推荐】
Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Sebastian Schelter and Maarten de Rijke
CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users【CAPTOR：面向外地用户的人群感知旅行前推荐系统】
Haoran Xin, Xinjiang Lu, Nengjun Zhu, Tong Xu, Dejing Dou and Hui Xiong
Deployable and Continuable Meta-Learning-Based Recommender System with Fast User-Incremental Updates【具有快速用户增量更新的可部署和可持续的基于元学习的推荐系统】
Renchu Guan, Haoyu Pang, Fausto Giunchiglia, Ximing Li, Xuefeng Yang and Xiaoyue Feng
Item Similarity Mining for Multi-Market Recommendation【商品相似性挖掘】
Jiangxia Cao, Xin Cong, Tingwen Liu and Bin Wang
ReLoop: A Self-Correction Learning Loop for Recommender Systems【ReLoop：推荐系统的自校正学习】
Guohao Cai, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Rui Zhang, Xiuqiang He and Ruiming Tang
Denoising Time Cycle Modeling for Recommendation【用于推荐的去噪时间周期建模】
Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen and Wenliang Zhong
Towards Results-level Proportionality for Multi-objective Recommender Systems【多目标推荐系统】
Ladislav Peska and Patrik Dokoupil
Regulating Provider Groups Exposure in Recommendations【调整商家组曝光】
Mirko Marras, Ludovico Boratto, Guilherme Ramos and Gianni Fenu
Rethinking Correlation-based Item-Item Similarities for Recommender Systems【重新思考推荐系统的基于相关性的项目相似性】
Katsuhiko Hayashi
A Content Recommendation Policy for Gaining Subscribers【内容推荐】
Konstantinos Theocharidis, Manolis Terrovitis, Spiros Skiadopoulos and Panagiotis Karras
Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems【基于 VAE 的推荐系统的目标多样化】
Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin and Scott Sanner