KDD2023推荐系统论文整理(研究专题）

张小磊

发布于 2023-08-22 18:57:26

8570

发布于 2023-08-22 18:57:26

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1416篇有效投稿，其中313篇论文被接收，接收率为22.10%，相比KDD2022的接收率14.98%有所上升。其中，涉及到的推荐系统相关的论文共35篇（本次只整理了Research Track相关论文，应用专题下次进行专门报道）。整理不易，欢迎小手点个在看/分享。

往年KDD推荐系统论文整理可参考：

本文收集与整理了发表在该会议上的推荐系统相关论文，以供研究者们提前一睹为快。本会议接受的论文主要整理了Research Track Papers，因此大家可以提前领略和关注学术界的最新动态。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，学术类论文官网接收论文列表如下：

https://kdd.org/kdd2023/research-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推荐系统中的公平性[5]、Bias问题[27,28,30]、对话推荐系统[2,3,4,30]、推荐中的隐私和安全问题[7,9]、基于图的推荐系统[18,19,20,21,22,23,32]、点击率预估问题[13,114]、序列推荐模型[12,17,33]等，与去年所关注的主题类似。

1. Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
2. Improving Conversational Recommendation Systems via Counterfactual Data Simulation
3. LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation
4. User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback
5. Path-Specific Counterfactual Fairness for Recommender Systems
6. Meta multi-agent exercise recommendation: A game application perspective
7. Shilling Black-box Review-based Recommender Systems through Fake Review Generation
8. Generalized Matrix Local Low Rank Representation by Random Projection and Submatrix Propagation
9. Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation
10. Off-Policy Evaluation of Ranking Policies under Diverse User Behavior
11. Generative Flow Network for Listwise Recommendation
12. Text Is All You Need: Learning Language Representations for Sequential Recommendation
13. MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
14. Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
15. PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
16. Efficient Bi-Level Optimization for Recommendation Denoising
17. Adaptive Disentangled Transformer for Sequential Recommendation
18. Meta Graph Learning for Long-tail Recommendation
19. Graph Neural Bandits
20. E-commerce Search via Content Collaborative Graph Neural Network
21. Criteria Tell you More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
22. Knowledge Graph Self-Supervised Rationalization for Recommendation
23. On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
24. Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
25. Hierarchical Invariant Learning for Domain Generalization Recommendation
26. UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
27. Debiasing Recommendation by Learning Identifiable Latent Confounders
28. Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective
29. Who should be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation
30. Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
31. A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation
32. Multiplex Heterogeneous Graph Neural Network with Behavior Pattern Modeling
33. Contrastive Learning for User Sequence Representation in Personalized Product Search
34. Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
35. Task Relation-aware Continual User Representation Learning

1. Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Yan Wen, Chen Gao, LINGLING YI, Liwei Qiu, Yaqing Wang, Yong Li

2. Improving Conversational Recommendation Systems via Counterfactual Data Simulation

Xiaolei Wang, Kun Zhou, Xinyu Tang, Xin Zhao, Fan Pan, Zhao Cao, Ji-Rong Wen

https://arxiv.org/pdf/2306.02842.pdf

3. LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation

Taeho Kim, Juwon Yu, Won-Yong Shin, Hyunyoung Lee, Ji-hui Im, Sang-Wook Kim

4. User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback

Yu Xia, Junda Wu, Tong Yu, Sungchul Kim, Ryan A. Rossi, Shuai Li

5. Path-Specific Counterfactual Fairness for Recommender Systems

Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li

https://arxiv.org/pdf/2306.02615.pdf

6. Meta multi-agent exercise recommendation: A game application perspective

Fei Liu, Xuegang Hu, Shuochen Liu, Chenyang Bu, Le Wu

https://le-wu.com/files/Publications/CONFERENCES/KDD-23-liu.pdf

7. Shilling Black-box Review-based Recommender Systems through Fake Review Generation

Hung-Yun Chiang, Yi-Syuan Chen, Yun-Zhu Song, Hong-Han Shuai, Jason S. Chang

https://arxiv.org/abs/2306.16526

8. Generalized Matrix Local Low Rank Representation by Random Projection and Submatrix Propagation

Pengtao Dang, Haiqi Zhu, Tingbo Guo, Changlin Wan, Tong Zhao, Paul Salama, Yijie Wang, Sha Cao, Chi Zhang

9. Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation

Zeyu Cao, Zhipeng Liang, Bingzhe Wu, Shu Zhang, Hangyu Li, Ouyang Wen, Yu Rong, Peilin Zhao

10. Off-Policy Evaluation of Ranking Policies under Diverse User Behavior

Haruka Kiyohara, Masatoshi Uehara, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito

https://arxiv.org/abs/2306.15098

11. Generative Flow Network for Listwise Recommendation

Shuchang Liu, Qingpeng Cai, Zhankui He, Bowen Sun, Julian McAuley, Dong Zheng, Peng Jiang, Kun Gai

https://arxiv.org/pdf/2306.02239.pdf

12. Text Is All You Need: Learning Language Representations for Sequential Recommendation

Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, Julian McAuley

https://arxiv.org/pdf/2305.13731.pdf

KDD2023 | RecFormer: 为序列推荐学习通用的语言表示

13. MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction

Jianghao Lin, Yanru Qu, Wei Guo, Xinyi Dai, Ruiming Tang, Yong Yu, Weinan Zhang

14. Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction

Runlong Yu, Xiang Xu, Yuyang Ye, Qi Liu, Enhong Chen

15. PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement

Wanqi Xue, Qingpeng Cai, Zhenghai Xue, Shuo Sun, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An

https://arxiv.org/abs/2212.02779

16. Efficient Bi-Level Optimization for Recommendation Denoising

Zongwei Wang, Min Gao, Wentao Li, Junliang Yu, LinXin Guo, Hongzhi Yin

https://arxiv.org/abs/2210.10321

17. Adaptive Disentangled Transformer for Sequential Recommendation

Yipeng Zhang, Xin Wang, Hong Chen, Wenwu Zhu

http://mn.cs.tsinghua.edu.cn/xinwang/PDF/papers/2023_Adaptive%20Disentangled%20Transformer%20for%20Sequential%20Recommendation.pdf

18. Meta Graph Learning for Long-tail Recommendation

Chunyu Wei, Jian Liang, Di Liu, Zehui Dai, Mang Li, Fei Wang

19. Graph Neural Bandits

Yunzhe Qi, Yikun Ban, Jingrui He

https://arxiv.org/abs/2207.06456

20. E-commerce Search via Content Collaborative Graph Neural Network

Guipeng Xv, Chen Lin, Wanxian Guan, Jinping Gou, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng

21. Criteria Tell you More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation

Jinduk Park, Siqing Li, Xin Cao, Won-Yong Shin

https://arxiv.org/pdf/2305.18885.pdf

22. Knowledge Graph Self-Supervised Rationalization for Recommendation

Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang

https://arxiv.org/pdf/2307.02759.pdf

23. On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering

Jiayan Guo, Lun Du, Xu Chen, Xiaojun Ma, Qiang Fu, Shi Han, Dongmei Zhang, Yan Zhang

24. Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek

25. Hierarchical Invariant Learning for Domain Generalization Recommendation

Zeyu Zhang, Heyang Gao, Hao Yang, Xu Chen

26. UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation

Jiacheng Li, Zhankui He, Jingbo Shang, Julian McAuley

https://arxiv.org/pdf/2209.13885.pdf

27. Debiasing Recommendation by Learning Identifiable Latent Confounders

Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang, Ruocheng Guo

https://arxiv.org/abs/2302.05052

28. Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective

Teng Xiao, Zhengyu Chen, Suhang Wang

29. Who should be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation

Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, Peng Cui

30. Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction

Yifan Wang, Peijie Sun, Min Zhang, Qinglin Jia, Jingjie Li, Shaoping Ma

31. A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation

Xiaotian Zhou, Liwang Zhu, Wei Li, Zhongzhi Zhang

32. Multiplex Heterogeneous Graph Neural Network with Behavior Pattern Modeling

Chaofan Fu, Guanjie Zheng, Chao Huang, Yanwei Yu, Junyu Dong

33. Contrastive Learning for User Sequence Representation in Personalized Product Search

Shitong Dai, Jiongnan Liu, Zhicheng Dou, Haonan Wang, Lin Liu, Bo Long, Ji-Rong Wen

34. Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling

Bei Yang, Jie Gu, Liu Ke, Xiaoxiao Xu, Renjun Xu, Qinghui Sun, Hong Liu

35. Task Relation-aware Continual User Representation Learning

Sein Kim, Namkyeong Lee, Donghyun Kim, Min-Chul Yang, Chanyoung Park

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