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KDD2023 Hot Recommendation Papers Full List
This blog summarizes the latest research development of Recommendation papers published in KDD2023 conferences. This year there are total 44 papers related to Recommendation in KDD2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).
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- 1.Generative Flow Network for Listwise Recommendation
- 2.PSLOG: Pretraining with Search Logs for Document Ranking
- 3.A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation
- 4.Text Is All You Need: Learning Language Representations for Sequential Recommendation
- 5.Efficient Bi-Level Optimization for Recommendation Denoising
- 6.Adaptive Disentangled Transformer for Sequential Recommendation
- 7.Meta Graph Learning for Long-Tail Recommendation
- 8.Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
- 9.Knowledge Graph Self-Supervised Rationalization for Recommendation
- 10.Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
- 11.Improving Conversational Recommendation Systems via Counterfactual Data Simulation
- 12.LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation
- 13.Delving into Global Dialogue Structures: Structure Planning Augmented Response Selection for Multi-Turn Conversations
- 14.Hierarchical Invariant Learning for Domain Generalization Recommendation
- 15.UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
- 16.Debiasing Recommendation by Learning Identifiable Latent Confounders
- 17.Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective
- 18.Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation
- 19.Contrastive Learning for User Sequence Representation in Personalized Product Search
- 20.Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation
- 21.Meta Multi-Agent Exercise Recommendation: A Game Application Perspective
- 22.UA-FedRec: Untargeted Attack on Federated News Recommendation
- 23.PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation
- 24.Hierarchical Projection Enhanced Multi-behavior Recommendation
- 25.AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
- 26.SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation
- 27.TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
- 28.Constrained Social Community Recommendation
- 29.Doctor Specific Tag Recommendation for Online Medical Record Management
- 30.M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation
- 31.CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
- 32.Multi-Channel Integrated Recommendation with Exposure Constraints
- 33.Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
- 34.A Collaborative Transfer Learning Framework for Cross-Domain Recommendation
- 35.ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
- 36.Fresh Content Needs More Attention: Multi-Funnel Fresh Content Recommendation
- 37.PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation
- 38.Counterfactual Video Recommendation for Duration Debiasing
- 39.Exploiting Intent Evolution in E-commercial Query Recommendation
- 40.Workplace Recommendation with Temporal Network Objectives
- 41.Modeling Dual Period-Varying Preferences for Takeaway Recommendation
- 42.Empowering Long-Tail Item Recommendation through Cross Decoupling Network
- 43.Adaptive Graph Contrastive Learning for Recommendation
- 44.Tree-Based Progressive Regression Model for Watch-Time Prediction in Short-Video Recommendation
Paper List
1.Generative Flow Network for Listwise Recommendation
Shuchang Liu (Kuaishou Technology), Qingpeng Cai (Kuaishou Technology), Zhankui He (University of California, San Diego), Sun Bowen (Peking University), Julian McAuley (University of California, San Diego), Dong Zheng (Kuaishou Technology), Peng Jiang (Kuaishou Technology), Kun Gai (Unaffiliated)
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2.PSLOG: Pretraining with Search Logs for Document Ranking
Zhan Su (Renmin University of China), Zhicheng Dou (Renmin University of China), Yujia Zhou (Renmin University of China), Ziyuan Zhao (Tencent), Ji-Rong Wen (Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education; Renmin University of China)
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3.A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation
Xiaotian Zhou (Fudan University), Liwang Zhu (Fudan University), Wei Li (Fudan University), Zhongzhi Zhang (Fudan University)
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4.Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li (University of California, San Diego), Ming Wang (Amazon), Jin Li (Amazon), Jinmiao Fu (Amazon), Xin Shen (Amazon), Jingbo Shang (University of California, San Diego), Julian McAuley (University of California, San Diego)
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5.Efficient Bi-Level Optimization for Recommendation Denoising
Zongwei Wang (University of Chongqing), Min Gao (University of Chongqing), Wentao Li (The Hong Kong University of Science and Technology (Guangzhou)), Junliang Yu (University of Queensland), Linxin Guo (University of Chongqing), Hongzhi Yin (University of Queensland)
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6.Adaptive Disentangled Transformer for Sequential Recommendation
Yipeng Zhang (Tsinghua University, Tsinghua University), Xin Wang (Tsinghua University, Tsinghua University), Hong Chen (Tsinghua University, Tsinghua University), Wenwu Zhu (Tsinghua University, Tsinghua University)
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7.Meta Graph Learning for Long-Tail Recommendation
Chunyu Wei (Tsinghua University), Jian Liang (Independent Researcher), Di Liu (Alibaba Group), Zehui Dai (Alibaba Group), Mang Li (Alibaba Group), Fei Wang (Cornell University)
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8.Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
Jin-Duk Park (Yonsei University), Siqing Li (The University of New South Wales), Xin Cao (The University of New South Wales), Won-Yong Shin (Yonsei University)
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9.Knowledge Graph Self-Supervised Rationalization for Recommendation
Yuhao Yang (The University of Hong Kong), Chao Huang (The University of Hong Kong), Lianghao Xia (The University of Hong Kong), Chunzhen Huang (Tencent)
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10.Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
Thomas M. McDonald (University of Manchester), Lucas Maystre (Spotify), Mounia Lalmas (Spotify), Daniel Russo (University of Columbia; Spotify), Kamil Ciosek (Spotify)
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11.Improving Conversational Recommendation Systems via Counterfactual Data Simulation
Xiaolei Wang (Renmin University of China), Kun Zhou (Renmin University of China), Xinyu Tang (Renmin University of China), Wayne Xin Zhao (Renmin University of China), Fan Pan (Huawei Poisson Lab), Zhao Cao (Huawei Poisson Lab), Ji-Rong Wen (Renmin University of China)
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12.LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation
Taeho Kim (Hanyang University), Juwon Yu (Hanyang University), Won-Yong Shin (Yonsei University), Hyunyoung Lee (KT Corporation), Ji-hui Im (KT Corporation), Sang-Wook Kim (Hanyang University)
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13.Delving into Global Dialogue Structures: Structure Planning Augmented Response Selection for Multi-Turn Conversations
Tingchen Fu (Gaoling School of AI (GSAI), Renmin University of China), Xueliang Zhao (Peking University), Rui Yan (Gaoling School of AI (GSAI), Renmin University of China; Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education)
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14.Hierarchical Invariant Learning for Domain Generalization Recommendation
Zeyu Zhang (Renmin University of China), Heyang Gao (Beijing University of Posts and Telecommunications), Hao Yang (Renmin University of China), Xu Chen (Renmin University of China)
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15.UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
Jiacheng Li (University of California, San Diego), Zhankui He (University of California, San Diego), Jingbo Shang (University of California, San Diego), Julian McAuley (University of California, San Diego)
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16.Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang (Hong Kong University of Science and Technology), Xiaoying Zhang (ByteDance Research), Yang Liu (ByteDance Research), Hongning Wang (University of Virginia), Min Gao (Chongqing University), Jiheng Zhang (Hong Kong University of Science and Technology), Ruocheng Guo (ByteDance Research)
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17.Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective
Teng Xiao (The Pennsylvania State University), Zhengyu Chen (Zhejiang University), Suhang Wang (The Pennsylvania State University)
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18.Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation
Haoxuan Li (Peking University), Chunyuan Zheng (University of California, San Diego), Peng Wu (Beijing Technology and Business University), Kun Kuang (Zhejiang University), Yue Liu (Renmin University of China), Peng Cui (Tsinghua University)
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19.Contrastive Learning for User Sequence Representation in Personalized Product Search
Shitong Dai (Renmin University of China), Jiongnan Liu (Renmin University of China), Zhicheng Dou (Renmin University of China), Haonan Wang (JD.com, Inc.), Lin Liu (JD.com, Inc.), Bo Long (JD.com, Inc.), Ji-Rong Wen (Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education)
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20.Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation
Zeyu Cao (University of Cambridge), Zhipeng Liang (Hong Kong University of Science and Technology), Bingzhe Wu (Tencent AI Lab), Shu Zhang (Tencent AI Lab), Hangyu Li (Tencent AI Lab), Ouyang Wen (Tencent AI Lab), Yu Rong (Tencent AI Lab), Peilin Zhao (Tencent AI Lab)
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21.Meta Multi-Agent Exercise Recommendation: A Game Application Perspective
Fei Liu (Hefei University of Technology), Xuegang Hu (Hefei University of Technology), Shuochen Liu (Hefei University of Technology), Chenyang Bu (Hefei University of Technology), Le Wu (Hefei University of Technology)
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22.UA-FedRec: Untargeted Attack on Federated News Recommendation
Jingwei Yi (University of Science and Technology of China), Fangzhao Wu (Microsoft Research Asia), Bin Zhu (Microsoft Research Asia), Jing Yao (Microsoft Research Asia), Zhulin Tao (Communication University of China), Guangzhong Sun (University of Science and Technology of China), Xin Xie (Microsoft Research Asia)
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23.PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation
Ruixuan Liu (Renmin University of China), Yang Cao (Hokkaido University), Yanlin Wang (Sun Yat-sun University), Lingjuan Lyu (Sony AI), Yun Chen (Shanghai University of Finance and Economics), Hong Chen (Renmin University of China)
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24.Hierarchical Projection Enhanced Multi-behavior Recommendation
Chang Meng (Tsinghua University), Hengyu Zhang (Tsinghua University), Wei Guo (Huawei Singapore Research Center), Huifeng Guo (Huawei Noahâ??s Ark Lab), Haotian Liu (Tsinghua University), Yingxue Zhang (Huawei Technologies Canada), Hongkun Zheng (Huawei Technologies Co Ltd), Ruiming Tang (Huawei Noahâ??s Ark Lab), Xiu Li (Tsinghua University), Rui Zhang (ruizhang.info)
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25.AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
Danwei Li (Meta AI), Zhengyu Zhang (Meta Platforms, Inc.), Siyang Yuan (Meta AI), Mingze Gao (Meta Platforms, Inc.), Weilin Zhang (Meta AI), Chaofei Yang (Meta AI), Xi Liu (Meta AI), Jiyan Yang (Meta AI)
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26.SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation
Zhaoxin Huan (Ant Group), Ang Li (Ant Group), Xiaolu Zhang (Ant Group), Xu Min (Ant Group), Jieyu Yang (Ant Group), Yong He (Ant Group), Jun Zhou (Ant Group)
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27.TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
Xue Xia (Pinterest), Pong Eksombatchai (Pinterest), Nikil Pancha (Pinterest), Dhruvil Deven Badani (Pinterest), Po-Wei Wang (Pinterest), Neng Gu (Pinterest), Saurabh Vishwas Joshi (Pinterest), Nazanin Farahpour (Pinterest), Zhiyuan Zhang (Pinterest), Andrew Zhai (Pinterest)
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28.Constrained Social Community Recommendation
Xingyi Zhang (The Chinese University of Hong Kong), Shuliang Xu (Tencent ), Wenqing Lin (Tencent ), Sibo Wang (The Chinese University of Hong Kong)
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29.Doctor Specific Tag Recommendation for Online Medical Record Management
Yejing Wang (City University of Hong Kong), Shen Ge (Tencent Jarvis Lab), Xiangyu Zhao (City University of Hong Kong), Xian Wu (Tencent Jarvis Lab), Tong Xu (University of Science and Technology of China), Chen Ma (City University of Hong Kong), Zhi Zheng (University of Science and Technology of China)
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30.M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation
Pengyu Zhao (Hulu Beijing), Xin Gao (Hulu Beijing), Chunxu Xu (Hulu Beijing), Liang Chen (Hulu Beijing)
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31.CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
Liu Chong (Tencent Inc.), Xiaoyang Liu (OPPO Inc.), Rongqin Zheng (Tencent Inc.), Lixin Zhang (Tencent Inc.), Xiaobo Liang (Soochow University), Juntao Li (Soochow University), Lijun Wu (Microsoft Research Asia), Min Zhang (Soochow University), Leyu Lin (Tencent Inc.)
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32.Multi-Channel Integrated Recommendation with Exposure Constraints
Yue Xu (Alibaba Group.), Qijie Shen (Alibaba Group.), Jianwen Yin (Alibaba Group.), Zengde Deng (Cainiao Network.), Dimin Wang (Alibaba Group.), Hao Chen (The Hong Kong Polytechnic University.), Lixiang Lai (Alibaba Group.), Tao Zhuang (Alibaba Group.), Junfeng Ge (Alibaba Group.)
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33.Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
Xiaohui Chen (Tufts University), Jiankai Sun (ByteDance Inc.), Taiqing Wang (ByteDance Inc.), Ruocheng Guo (ByteDance Research), Li-Ping Liu (Tufts University), Aonan Zhang (Apple Inc.)
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34.A Collaborative Transfer Learning Framework for Cross-Domain Recommendation
Wei Zhang (Meituan), Pengye Zhang (Meituan), Bo Zhang (Meituan), Xingxing Wang (Meituan), Dong Wang (Meituan)
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35.ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
Jieming Zhu (Huawei Noahâ??s Ark Lab), Guohao Cai (Huawei Noahâ??s Ark Lab), Junjie Huang (Shanghai Jiao Tong University), Zhenhua Dong (Huawei Noahâ??s Ark Lab), Ruiming Tang (Huawei Noahâ??s Ark Lab), Weinan Zhang (Shanghai Jiao Tong University)
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36.Fresh Content Needs More Attention: Multi-Funnel Fresh Content Recommendation
Jianling Wang (Google), Haokai Lu (Google), Sai Zhang (Google), Bart Locanthi (Google), Haoting Wang (Google), Dylan Greaves (Google), Benjamin Lipshitz (Google), Sriraj Badam (Google), Ed Chi (Google), Cristos Goodrow (Google), Su-Lin Wu (Google), Lexi Baugher (Google), Minmin Chen (Google)
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37.PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation
Xuewu Jiao (Baidu Inc.), Weibin Li (Baidu Inc.), Xinxuan Wu (Baidu Inc.), Wei Hu (Baidu Inc.), Miao Li (Baidu Inc.), Jiang Bian (Baidu Inc.), Siming Dai (Baidu Inc.), Xinsheng Luo (Baidu Inc.), Mingqing Hu (Baidu Inc.), Zhengjie Huang (Baidu Inc.), Danlei Feng (Baidu Inc.), Junchao Yang (Baidu Inc.), Shikun Feng (Baidu Inc.), Haoyi Xiong (Baidu Inc.), Dianhai Yu (Baidu Inc.), Shuanglong Li (Baidu Inc.), Jingzhou He (Baidu Inc.), Yanjun Ma (Baidu Inc.), Lin Liu (Baidu Inc.)
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38.Counterfactual Video Recommendation for Duration Debiasing
Shisong Tang (Tsinghua University ), Qing Li (Peng Cheng Laboratory), Dingmin Wang (University of Oxford), Ci Gao (Jilin university), Wentao Xiao (Tsinghua University ), Dan Zhao (Peng Cheng Laboratory), Yong Jiang (Tsinghua University), Qian Ma (ByteDance Inc.), Aoyang Zhang (ByteDance Inc.)
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39.Exploiting Intent Evolution in E-commercial Query Recommendation
Yu Wang (University of Illinois Chicago), Zhengyang Wang (Amazon), Hengrui Zhang (University of Illinois Chicago), Qingyu Yin (Amazon), Xianfeng Tang (Amazon), Yinghan Wang (Amazon), Danqing Zhang (Amazon), Limeng Cui (Amazon), Monica Cheng (Amazon), Bing Yin (Amazon), Suhang Wang (Amazon), Philip S. Yu (University of Illinois Chicago)
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40.Workplace Recommendation with Temporal Network Objectives
Kiran Tomlinson (Cornell University), Jennifer Neville (Microsoft), Longqi Yang (Microsoft), Mengting Wan (Microsoft), Cao Lu (Microsoft)
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41.Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Yuting Zhang (Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences), Yiqing Wu (Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences), Ran Le (Meituan), Yongchun Zhu (Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences), Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University), Ruidong Han (Meituan), Xiang Li (Unaffiliated), Wei Lin (Unaffiliated), Zhulin An (Institute of Computing Technology, Chinese Academy of Sciences), Yongjun Xu (Institute of Computing Technology, Chinese Academy of Sciences)
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42.Empowering Long-Tail Item Recommendation through Cross Decoupling Network
Yin Zhang (Google Research, Brain Team), Ruoxi Wang (Google Research, Brain Team), Derek Zhiyuan Cheng (Google Research, Brain Team), Tiansheng Yao (Google Research, Brain Team), Xinyang Yi (Google Research, Brain Team), Lichan Hong (Google Research, Brain Team), James Caverlee (Texas AM University), Ed H. Chi (Google Research, Brain Team)
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43.Adaptive Graph Contrastive Learning for Recommendation
Yangqin Jiang (University of Hong Kong), Chao Huang (University of Hong Kong), Lianghao Xia (University of Hong Kong)
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44.Tree-Based Progressive Regression Model for Watch-Time Prediction in Short-Video Recommendation
Xiao Lin (Kuaishou Technology), Xiaokai Chen (Kuaishou Technology), LInfeng Song (Kuaishou Technology), Jingwei Liu (Kuaishou Technology), Biao Li (Kuaishou Technology), Peng Jiang (Kuaishou Technology)
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