WWW2022推荐系统/计算广告论文集锦
嘿，記得給“機器學習與推薦算法”添加星標
| 作者：朱勇椿?
| 單位：中國科學院大學
| 研究方向：推薦系統、遷移學習、元學習
WWW 2022組委會近日放出了正式接收論文清單。大會共收到了1822篇論文，接收323篇，錄用率為17.7%。完整清單見：
www2022.thewebconf.org/accepted-papers/
下圖為WWW會議歷年論文投稿量與接收率統計圖，可以看出投稿量和接收率大體上呈現出每年都有新增的趨勢。今年的接收率相比于去年有大幅度下降，但投稿量有所提高。
近幾年，推薦系統和計算廣告一直是WWW上熱門主題，廣泛受到了學術界和業界的關注。本文整理了WWW2022上推薦系統和計算廣告方向的論文（topic的劃分主要根據本人的閱讀習慣，如有不合適的地方，歡迎指出）。
本文主要整理了推薦系統和計算廣告方面論文，共計74篇。其中時序推薦13篇、基于圖的推薦9篇、可解釋推薦7篇、推薦系統中的bias 5篇、因果相關4篇、公平性和隱私保護4、強化學習3篇、冷啟動3篇、基于自編碼機3篇、跨領域2篇、多任務2篇、對比學習3篇、計算廣告6篇、延遲反饋2篇、新聞推薦2篇、其他12篇。可以看到今年時序推薦和可解釋推薦大熱門，而技術方面，圖網絡和因果推理依然火爆。
時序推薦
Disentangling Long and Short-Term Interests for Recommendation
Yu Zheng, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Depeng Jin and Yong Li
Efficient Online Learning to Rank for Sequential Music Recommendation
Pedro Chaves, Bruno Pereira and Rodrygo Santos
Filter-enhanced MLP is All You Need for Sequential Recommendation
Kun Zhou, Hui Yu, Wayne Xin Zhao and Ji-Rong Wen
Generative Session-based Recommendation
Wang Zhidan, Ye Wenwen, Chen Xu, Zhang Wenqiang, Wang Zhenlei, Zou Lixin and Liu Weidong
GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction
Chunyu Wei, Bing Bai, Kun Bai and Fei Wang
Intent Contrastive Learning for Sequential Recommendation
Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley and Caiming Xiong
Learn from Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data
Jiarui Jin, Xianyu Chen, Weinan Zhang, Junjie Huang, Ziming Feng and Yong Yu
Sequential Recommendation via Stochastic Self-Attention
Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng and Philip S. Yu
Sequential Recommendation with Decomposed Item Feature Routing
Kun Lin, Zhenlei Wang, Zhipeng Wang, Bo Chen, Shiqi Shen and Xu Chen
Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation
Mingyue Cheng, Zhiding Liu, Qi Liu, Shenyang Ge and Enhong Chen
Unbiased Sequential Recommendation with Latent Confounders
Zhenlei Wang, Shiqi Shen, Zhipeng Wang, Bo Chen, Xu Chen and Ji-Rong Wen
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Shengyu Zhang, Lingxiao Yang, Dong Yao, Yujie Lu, Fuli Feng, Zhou Zhao, Tat-Seng Chua and Fei Wu
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
Qijie Shen, Hong Wen, Wanjie Tao, Jing Zhang, Fuyu Lv, Zulong Chen and Zhao Li
基于圖的推薦
FIRE: Fast Incremental Recommendation with Graph Signal Processing
Jiafeng Xia, Dongsheng Li, Hansu Gu, Jiahao Liu, Tun Lu and Ning Gu
Graph Based Extractive Explainer for Recommendations
Peng Wang, Renqin Cai and Hongning Wang
Graph Neural Transport Networks with Non-local Attentions for Recommender Systems
Huiyuan Chen, Chin-Chia Michael Yeh, Fei Wang and Hao Yang
Hypercomplex Graph Collaborative Filtering
Anchen Li, Bo Yang, Huan Huo and Farookh Hussain
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
Zihan Lin, Changxin Tian, Yupeng Hou and Wayne Xin Zhao
Revisiting Graph Neural Network based Social Recommendation
Ye Tao, Ying Li, Su Zhang, Zhirong Hou and Zhonghai Wu
STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation
Zhen Yang, Ming Ding, Bin Xu, Hongxia Yang and Jie Tang
VisGNN: Personalized Visualization Recommendation via Graph Neural Networks
Fayokemi Ojo, Ryan Rossi, Jane Hoffswell, Shunan Guo, Fan Du, Sungchul Kim, Chang Xiao and Eunyee Koh
Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
Liangwei Yang, Zhiwei Liu, Yu Wang, Chen Wang, Ziwei Fan and Philip Yu
可解釋
ExpScore: Learning Metrics for Recommendation Explanation (short paper)
Bingbing Wen, Yunhe Feng, Yongfeng Zhang and Chirag Shah
Path Language Modeling over Knowledge Graphs for Explainable Recommendation
Shijie Geng, Zuohui Fu, Juntao Tan, Yingqiang Ge, Gerard de Melo and Yongfeng Zhang
Graph Based Extractive Explainer for Recommendations
Peng Wang, Renqin Cai and Hongning Wang
Accurate and Explainable Recommendation via Review Rationalization
Sicheng Pan, Dongsheng Li, Hansu Gu, Tun Lu, Xufang Luo and Ning Gu
AmpSum: Adaptive Multiple-Product Summarization towards Improving Recommendation Explainability
Quoc-Tuan Truong, Tong Zhao, Chenghe Yuan, Jin Li, Jim Chan, Soo-Min Pantel and Hady W. Lauw
Comparative Explanations of Recommendations
Aobo Yang, Nan Wang, Renqin Cai, Hongbo Deng and Hongning Wang
Neuro-Symbolic Interpretable Collaborative Filtering for Attribute-based Recommendation
Wei Zhang, Junbing Yan, Zhuo Wang and Jianyong Wang
因果
Causal Representation Learning for Out-of-Distribution Recommendation
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin and Tat-Seng Chua
A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
Zihua Si, Xueran Han, Xiao Zhang, Jun Xu, Yue Yin, Yang Song and Ji-Rong Wen
Causal Preference Learning for Out-of-Distribution Recommendation
Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui and Yong Jiang
Learning to Augment for Casual User Recommendation
Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed Chi and Minmin Chen
公平性、隱私保護
Link Recommendations for PageRank Fairness
Sotiris Tsioutsiouliklis, Konstantinos Semertzidis, Evaggelia Pitoura and Panayiotis Tsaparas
FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
Jie Li, Yongli Ren and Ke Deng
Recommendation Unlearning
Chong Chen, Fei Sun, Min Zhang and Bolin Ding
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
Chaochao Chen, Huiwen Wu, Jiajie Su, Lingjuan Lyu, Xiaolin Zheng and Li Wang
推薦系統中的bias
CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction
Zhi Zheng, Zhaopeng Qiu, Tong Xu, Xian Wu, Xiangyu Zhao, Enhong Chen and Hui Xiong
Cross Pairwise Ranking for Unbiased Item Recommendation
Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo and Ruiming Tang
Rating Distribution Calibration for Selection Bias Mitigation in Recommendations
Haochen Liu, Da Tang, Ji Yang, Xiangyu Zhao, Hui Liu, Jiliang Tang and Youlong Cheng
UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation
Zixuan Xu, Penghui Wei, Weimin Zhang, Shaoguo Liu, Liang Wang and Bo Zheng
Unbiased Sequential Recommendation with Latent Confounders
Zhenlei Wang, Shiqi Shen, Zhipeng Wang, Bo Chen, Xu Chen and Ji-Rong Wen
跨領域
Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
Weiming Liu, Xiaolin Zheng, Mengling Hu and Chaochao Chen
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
Chaochao Chen, Huiwen Wu, Jiajie Su, Lingjuan Lyu, Xiaolin Zheng and Li Wang
多任務
Improving Personalized Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks
Yun He, Xue Feng, Cheng Cheng, Geng Ji, Yunsong Guo and James Caverlee
A Contrastive Sharing Model for Multi-Task Recommendation
Ting Bai, Yudong Xiao, Bin Wu, Guojun Yang, Hongyong Yu and Jian-Yun Nie
強化學習
Multi-level Recommendation Reasoning over Knowledge Graphs with Reinforcement Learning
Xiting Wang, Kunpeng Liu, Dongjie Wang, Le Wu, Yanjie Fu and Xing Xie
Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long and Jian Pei
Off-policy Learning over Heterogeneous Information for Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu and Guandong Xu
對比學習
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
Zihan Lin, Changxin Tian, Yupeng Hou and Wayne Xin Zhao
A Contrastive Sharing Model for Multi-Task Recommendation
Ting Bai, Yudong Xiao, Bin Wu, Guojun Yang, Hongyong Yu and Jian-Yun Nie
Intent Contrastive Learning for Sequential Recommendation
Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley and Caiming Xiong
冷啟動
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
Xiaoxiao Xu, Chen Yang, Qian Yu, Zhiwei Fang, Jiaxing Wang, Chaosheng Fan, Yang He, Changping Peng, Zhangang Lin and Jingping Shao
PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation
Haoyu Pang, Fausto Giunchiglia, Ximing Li, Renchu Guan and Xiaoyue Feng
KoMen: Domain Knowledge Guided Interaction Recommendation for Emerging Scenarios
Yiqing Xie, Zhen Wang, Carl Yang, Yaliang Li, Bolin Ding, Hongbo Deng and Jiawei Han
自編碼機
Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems
Yaochen Zhu and Zhenzhong Chen
Stochastic-Expert Variational Autoencoder for Collaborative Filtering
Yoon-Sik Cho and Min-hwan Oh
Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
Jin Chen, Binbin Jin, Xu Huang, Defu Lian, Kai Zheng and Enhong Chen
計算廣告
Equilibria in Auctions with Ad Types
Hadi Elzayn, Riccardo Colini Baldeschi, Brian Lan and Okke Schrijvers
Calibrated Click-Through Auctions
Dirk Bergemann, Paul Duetting, Renato Paes Leme and Song Zuo
On Designing a Two-stage Auction for Online Advertising
Yiqing Wang, Xiangyu Liu, Zhenzhe Zheng, Zhilin Zhang, Miao Xu, Chuan Yu and Fan Wu
Price Manipulability in First-Price Auctions
Johannes Brustle, Paul Duetting and Balasubramanian Sivan
Cross DQN: Cross Deep Q Network for Ads Allocation in Feed
Guogang Liao, Ze Wang, Xiaoxu Wu, Xiaowen Shi, Chuheng Zhang, Yongkang Wang, Xingxing Wang and Dong Wang
Investigating Advertisers\’ Domain-changing Behaviors and Their Impacts on Ad-blocker Filter Lists
Su-Chin Lin, Kai-Hsiang Chou, Yen Chen, Hsu-Chun Hsiao, Darion Cassel, Lujo Bauer and Limin Jia
延遲反饋
Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction
Yu Chen, Jiaqi Jin, Hui Zhao, Pengjie Wang, Guojun Liu, Jian Xu and Bo Zheng
Adaptive Experimentation with Delayed Binary Feedback
Zenan Wang, Carlos Carrion, Xiliang Lin, Fuhua Ji, Yongjun Bao and Weipeng Yan
新聞推薦
MINDSim: User Simulator for News Recommenders
Xufang Luo, Zheng Liu, Shitao Xiao, Xing Xie and Dongsheng Li
FeedRec: News Feed Recommendation with Various User Feedbacks
Chuhan Wu, Fangzhao Wu, Tao Qi, Qi Liu, Xuan Tian, Jie Li, Wei He, Yongfeng Huang and Xing Xie
其他
Distributionally-robust Recommendations for Improving Worst-case User Experience (short paper)
Hongyi Wen, Xinyang Yi, Tiansheng Yao, Jiaxi Tang, Lichan Hong and Ed H. Chi
Following Good Examples – Health Goal-Oriented Food Recommendation based on Behavior Data
Yabo Ling, Jian-Yun Nie, Daiva Nielsen, Barbel Knauper, Nathan Yang and Laurette Dubé
Learning Explicit User Interest Boundary for Recommendation
Jianhuan Zhuo, Qiannan Zhu, Yinliang Yue and Yuhong Zhao
Automating Feature Selection in Deep Recommender Systems
Yejing Wang, Xiangyu Zhao, Tong Xu and Xian Wu
Choice of Implicit Signal Matters: Accounting for UserAspirations in Podcast Recommendations
Zahra Nazari, Praveen Chandar, Ghazal Fazelnia, Catie Edwards, Benjamin Carterette and Mounia Lalmas
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
Seongku Kang, Dongha Lee, Wonbin Kweon, Junyoung Hwang and Hwanjo Yu
Deep Unified Representation for Heterogeneous Recommendation
Chengqiang Lu, Mingyang Yin, Shuheng Shen, Luo Ji, Qi Liu and Hongxia Yang
HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization
Menglin Yang, Min Zhou, Jiahong Liu, Defu Lian and Irwin King
Learning Recommenders for Implicit Feedback with Importance Resampling
Jin Chen, Binbin Jin, Defu Lian, Kai Zheng and Enhong Chen
Learning Robust Recommenders through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng, Jeoman Jose, Fuli Feng and Xiangnan He
Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation
Tengyue Han, Pengfei Wang, Shaozhang Niu and Chenliang Li
Rewiring what-to-watch-next Recommendations to Reduce Radicalization Pathways
Francesco Fabbri, Yanhao Wang, Francesco Bonchi, Carlos Castillo and Michael Mathioudakis
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