KDD2023推荐系统论文整理(应用专题）

第29届SIGKDD会议将于2023年8月6日至10日在美国加州长滩举行。据统计，今年共有725篇有效短文投稿，其中184篇论文被接收，接收率为25.37%，相比长文的22.10%有所降低。其中，涉及到的推荐系统相关的论文共35篇（本次只整理了ADS Track相关论文）。整理不易，欢迎小手点个在看/分享。

KDD2023研究型专题的推荐系统论文整理可移步：

KDD2023推荐系统论文整理(研究专题）

本次主要整理了应用专题的论文，因此大家可以提前领略和关注工业界在应用推荐系统方面的最新动态。如果不放心本文整理的推荐系统论文集锦，也可自行前往官网查看，应用类论文官网接收论文列表如下：

https://kdd.org/kdd2023/ads-track-papers/

通过对本次接收的论文进行总结发现，从所涉及的研究主题角度来看，此次大会主要聚焦在了语言模型、计算广告、重排序、点击率预估、多场景/多任务、隐私保护、图机器学习等；

通过对论文所在的组织机构总结发现，大部分论文发表于一线大厂，比如国外的谷歌、微软、Pinterest、Meta、苹果、雅虎等，以及国内的阿里、百度、快手、美团、华为、腾讯、字节等。所涵盖的应用主要聚焦在了短视频推荐、计算广告、智慧医疗、跨域推荐、新闻推荐、外卖推荐等场景。

• 1. A Collaborative Transfer Learning Framework for Cross-domain Recommendation
• 2. Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
• 3. UA-FedRec: Untargeted Attack on Federated News Recommendation
• 4. PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation
• 5. Doctor Specific Tag Recommendation for Online Medical Record Management
• 6. Hierarchical Projection Enhanced Multi-behavior Recommendation
• 7. AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
• 8. Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction
• 9. SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation
• 10. TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
• 11. M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation
• 12. CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
• 13. Multi-channel Integrated Recommendation with Exposure Constraints
• 14.Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
• 15. Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
• 16.Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
• 17. RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads
• 18.PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation
• 19.Counterfactual Video Recommendation for Duration Debiasing
• 20.Exploiting Intent Evolution in E-commercial Query Recommendation
• 21.Workplace Recommendation with Temporal Network Objectives
• 22.Modeling Dual Period-Varying Preferences for Takeaway Recommendation
• 23.Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
• 24.Adaptive Graph Contrastive Learning for Recommendation
• 25.BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment
• 26.Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
• 27.PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
• 28.BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction
• 29.A Personalized Automated Bidding Framework for Fairness-aware Online Advertising
• 30.A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling
• 31.PASS: Personalized Advertiser-aware Sponsored Search
• 32.Controllable Multi-Objective Re-ranking with Policy Hypernetworks
• 33.On-device Integrated Re-ranking with Heterogeneous Behavior Modeling
• 34.TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
• 35. Multi-factor Sequential Re-ranking with Perception-Aware Diversification
• 36. Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
• 37. Extreme Multi-Label Classification for Ad Targeting using Factorization Machines
• 38. Deep Landscape Forecasting in Multi-Slot Real-Time Bidding
• 39. Towards Fairness in Personalized Ads Using Impression Variance aware Reinforcement Learning

1. A Collaborative Transfer Learning Framework for Cross-domain Recommendation

Wei Zhang (Meituan), Pengye Zhang (Meituan), Bo Zhang (Meituan), Xingxing Wang (Meituan), Dong Wang (Meituan)

https://arxiv.org/abs/2306.16425

2. Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop

Jieming Zhu (Huawei Noah's Ark Lab), Guohao Cai (Huawei Noah's Ark Lab), Junjie Huang (Shanghai Jiao Tong University), Zhenhua Dong (Huawei Noah's Ark Lab), Ruiming Tang (Huawei Noah's Ark Lab), Weinan Zhang (Shanghai Jiao Tong University)

https://arxiv.org/abs/2306.08808

3. 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)

https://arxiv.org/abs/2202.06701

4. 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)

https://arxiv.org/abs/2204.08146

5. 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)

6. Hierarchical Projection Enhanced Multi-behavior Recommendation

Chang Meng (Tsinghua University), Hengyu Zhang (Tsinghua University), Wei Guo (Huawei Singapore Research Center), Huifeng Guo (Huawei Noah's Ark Lab), Haotian Liu (Tsinghua University), Yingxue Zhang (Huawei Technologies Canada), Hongkun Zheng (Huawei Technologies Co Ltd), Ruiming Tang (Huawei Noah's Ark Lab), Xiu Li (Tsinghua University), Rui Zhang (ruizhang.info)

7. 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)

https://arxiv.org/abs/2304.04959

8. Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction

Erxue Min (Independent Researcher), Da Luo (Weixin Open Platform, Tencent), Kangyi Lin (Weixin Open Platform, Tencent), Chunzhen Huang (Weixin Open Platform, Tencent), Yang Liu (The Hong Kong University of Science and Technology)

9. 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)

10. 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)

https://arxiv.org/abs/2306.00248

11. 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)

https://arxiv.org/abs/2306.00248

12. 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.)

13. 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.)

https://arxiv.org/abs/2305.12319

14.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.)

https://arxiv.org/abs/2305.16391

15. Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach

Zhangming Chan (Alibaba Group), Yu Zhang (Alibaba Group), Shuguang Han (Alibaba Group), Yong Bai (Nanjing University), Xiang-Rong Sheng (Alibaba Group), Siyuan Lou (Alibaba Group), Jiacen Hu Hu (University of Science and Technology Beijing), Baolin Liu (University of Science and Technology Beijing), Yuning Jiang (Alibaba Group), Jian Xu (Alibaba Group), Bo Zheng (Alibaba Group)

16.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)

https://arxiv.org/abs/2305.16391

17. RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads

Penghui Wei (Alibaba Group), Yongqiang Chen (Alibaba Group), ShaoGuo Liu (Alibaba Group), Liang Wang (Alibaba Group), Bo Zheng (Alibaba Group)

18.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.)

19.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.)

20.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)

https://www.amazon.science/publications/exploiting-intent-evolution-in-e-commercial-query-recommendation

21.Workplace Recommendation with Temporal Network Objectives

Kiran Tomlinson (Cornell University), Jennifer Neville (Microsoft), Longqi Yang (Microsoft), Mengting Wan (Microsoft), Cao Lu (Microsoft)

22.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)

https://arxiv.org/abs/2306.04370

23.Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

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)

https://arxiv.org/abs/2210.14309

24.Adaptive Graph Contrastive Learning for Recommendation

Yangqin Jiang (University of Hong Kong), Chao Huang (University of Hong Kong), Lianghao Xia (University of Hong Kong)

https://arxiv.org/abs/2305.10837

25.BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment

Xiao Hu (BOSS Zhipin), Yuan Cheng (BOSS Zhipin), Zhi Zheng (University of Science and Technology of China; BOSS Zhipin), Yue Wang (BOSS Zhipin), Xinxin Chi (BOSS Zhipin), Hengshu Zhu (BOSS Zhipin)

26.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)

https://arxiv.org/pdf/2306.03392.pdf

27.PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce

Xiaowen Shi (Meituan), Fan Yang (Meituan), Ze Wang (Meituan), Xiaoxu Wu (Meituan), Muzhi Guan (Meituan), Guogang Liao (Meituan), Wang Yongkang (Meituan), Xingxing Wang (Meituan), Dong Wang (Meituan)

https://arxiv.org/pdf/2302.03487.pdf

28.BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction

Dong Wang (STCA, Microsoft Corporation), Kav Salamatian (University of Savoie), Yunqing Xia (STCA, Microsoft Corporation), Weiwei Deng (STCA, Microsoft Corporation), Qi Zhang (STCA, Microsoft Corporation)

29.A Personalized Automated Bidding Framework for Fairness-aware Online Advertising

Haoqi Zhang (Shanghai Jiao Tong University), Lvyin Niu (Alibaba Group), Zhenzhe Zheng (Shanghai Jiao Tong University), Zhilin Zhang (Alibaba Group), Shan Gu (Alibaba Group), Fan Wu (Shanghai Jiao Tong University), Chuan Yu (Alibaba Group), Jian Xu (Alibaba Group), Guihai Chen (Shanghai Jiao Tong University), Bo Zheng (Alibaba Group)

30.A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling

Xianghui Zhu (Shanghai Jiao Tong University), Peng Du (Alibaba Group), Shuo Shao (Shanghai Jiao Tong University), Chenxu Zhu (Shanghai Jiao Tong University), Weinan Zhang (Shanghai Jiao Tong University), Yang Wang (East China Normal University), Yang Cao (Alibaba Group)

31.PASS: Personalized Advertiser-aware Sponsored Search

Zhoujin Tian (Microsoft), Chaozhuo Li (Microsoft Research Asia), Zhiqiang Zuo (Microsoft), Zengxuan Wen (Microsoft), Lichao Sun (Lehigh University), Xinyue Hu (Microsoft), Wen Zhang (Microsoft), Haizhen Huang (Microsoft), Senzhang Wang (Central South University), Weiwei Deng (Microsoft), Xing Xie (Microsoft Research Asia), Qi Zhang (Microsoft)

32.Controllable Multi-Objective Re-ranking with Policy Hypernetworks

Sirui Chen (Renmin University of China), Yuan Wang (Alibaba Group), Zijing Wen (Alibaba Group), Zhiyu Li (Alibaba Group), Changshuo Zhang (Renmin University of China), Xiao Zhang (Renmin University of China), Quan Lin (Alibaba Group), Cheng Zhu (Alibaba Group), Jun Xu (Renmin University of China)

33.On-device Integrated Re-ranking with Heterogeneous Behavior Modeling

Yunjia Xi (Shanghai Jiao Tong University), Weiwen Liu (Huawei Noah's Ark Lab), Yang Wang (East China Normal University), Ruiming Tang (Huawei Noah's Ark Lab), Weinan Zhang (Shanghai Jiao Tong University), Yue Zhu (Consumer Business Group, Huawei), Rui Zhang (ruizhang.info), Yong Yu (Shanghai Jiao Tong University)

34.TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou

Jianxin Chang (Kuaishou Technology), Chenbin Zhang (Kuaishou Technology), Zhiyi Fu (Kuaishou Technology), Xiaoxue Zang (Kuaishou Technology), Lin Guan (Kuaishou Technology), Jing Lu (Kuaishou Technology), Yiqun Hui (Kuaishou Technology), Dewei Leng (Kuaishou Technology), Yanan Niu (Kuaishou Technology), Yang Song (Kuaishou Technology), Kun Gai (Unaffiliated)

35. Multi-factor Sequential Re-ranking with Perception-Aware Diversification

Yue Xu (Alibaba Group), Hao Chen (The Hong Kong Polytechnic University), Zefan Wang (Jinan University), Jianwen Yin (Alibaba Group), Qijie Shen (Alibaba Group), Dimin Wang (Alibaba Group), Feiran Huang (Jinan University), Lixiang Lai (Alibaba Group), Tao Zhuang (Alibaba Group), Junfeng Ge (Alibaba Group), Xia Hu (Rice University)

36. Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model

Xiang-Rong Sheng (Alibaba Group), Jingyue Gao (Alibaba Group), Yueyao Cheng (Alibaba Group), Siran Yang (Alibaba Group), Shuguang Han (Alibaba Group), Hongbo Deng (Alibaba Group), Yuning Jiang (Alibaba Group), Jian Xu (Alibaba Group), Bo Zheng (Alibaba Group)

37. Extreme Multi-Label Classification for Ad Targeting using Factorization Machines

Martin Pavlovski (Yahoo Research), Srinath Ravindran (Yahoo Research), Djordje Gligorijevic (eBay), Shubham Agrawal (Yahoo Research), Ivan Stojkovic (Yahoo Research), Nelson Segura-Nunez (Yahoo Inc.), Jelena Gligorijevic (Yahoo Research)

38. Deep Landscape Forecasting in Multi-Slot Real-Time Bidding

Weitong Ou (Shanghai Jiao Tong University), Bo Chen (Huawei Noah’s Ark Lab), Yingxuan Yang (Shanghai Jiao Tong University), Xinyi Dai (Shanghai Jiao Tong University), Weiwen Liu (Huawei Noah’s Ark Lab), Weinan Zhang (Shanghai Jiao Tong University), Ruiming Tang (Huawei Noah’s Ark Lab), Yong Yu (Shanghai Jiao Tong University)

39. Towards Fairness in Personalized Ads Using Impression Variance aware Reinforcement Learning

Aditya Srinivas Timmaraju (Meta), Mehdi Mashayekhi (Meta), Mingliang Chen (Meta), Qi Zeng (Meta), Quintin Fettes (Meta), Wesley Cheung (Meta), Yihan Xiao (Meta), Manojkumar Rangasamy Kannadasan (Meta), Pushkar Tripathi (Meta), Sean Gahagan (Meta), Miranda Bogen (Meta), Rob Roudani (Meta)