WWW2022推荐系统/计算广告论文集锦
嘿，记得给“机器学习与推荐算法”添加星标
| 作者：朱勇椿
| 单位：中国科学院大学
| 研究方向：推荐系统、迁移学习、元学习
WWW 2022组委会近日放出了正式接收论文清单。大会共收到了1822篇论文，接收323篇，录用率为17.7%。完整清单见：
www2022.thewebconf.org/accepted-papers/
下图为WWW会议历年论文投稿量与接收率统计图，可以看出投稿量和接收率大体上呈现出每年都有新增的趋势。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。
近几年，推荐系统和计算广告一直是WWW上热门主题，广泛受到了学术界和业界的关注。本文整理了WWW2022上推荐系统和计算广告方向的论文（topic的划分主要根据本人的阅读习惯，如有不合适的地方，欢迎指出）。
本文主要整理了推荐系统和计算广告方面论文，共计74篇。其中时序推荐13篇、基于图的推荐9篇、可解释推荐7篇、推荐系统中的bias 5篇、因果相关4篇、公平性和隐私保护4、强化学习3篇、冷启动3篇、基于自编码机3篇、跨领域2篇、多任务2篇、对比学习3篇、计算广告6篇、延迟反馈2篇、新闻推荐2篇、其他12篇。可以看到今年时序推荐和可解释推荐大热门，而技术方面，图网络和因果推理依然火爆。
时序推荐
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
基于图的推荐
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
可解释
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、隐私保护
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
推荐系统中的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
跨领域
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
多任务
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
强化学习
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
对比学习
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
冷启动
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
自编码机
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
计算广告
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
延迟反馈
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
新闻推荐
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
欢迎干货投稿 \ 论文宣传 \ 合作交流
推荐阅读
WSDM22@教程 | 基于图神经网络的推荐系统
基于图的推荐系统进展总结, 共包含11篇文献
WSDM2022 | 数据困境: 我们究竟有多了解推荐系统数据集？
由于公众号试行乱序推送，您可能不再准时收到机器学习与推荐算法的推送。为了第一时间收到本号的干货内容， 请将本号设为星标，以及常点文末右下角的“在看”。
喜欢的话点个在看吧
1.学习之路 1.1.基础阶段 参考资料:[1]<推荐系统实践>,项亮:[2]<推荐系统开发实战>,高阳团:[3]<美团机器学习实践>,美团算法团队:[4]< ... http://www.huxmarket.com/detail/2966 DSP场景假定前提: 以CTR预估为例,向广告主以CPC(OCPC)方式收费,向ADX以CPM方式付费.投放计划受预算限制,在 ... 作者 | GuoXun 整理 | NewBeeNLP KDD2021 放榜,其中research track共收到了1541篇投稿,接收了238篇长文,Applied Data Science ... 嘿,记得给"机器学习与推荐算法"添加星标 一年一度的知识发现与数据挖掘顶级会议SIGKDD将于8月14日至18日在线上举行.据统计,今年共有1541篇有效投稿,其中238篇论文被接 ... 嘿,记得给"机器学习与推荐算法"添加星标 一年一度的知识发现与数据挖掘顶级会议SIGKDD将于8月14日至18日在线上举行.据统计,今年共有1541篇有效投稿,其中238篇论文被接 ... 计算广告与推荐系统有哪些区别? 这两个领域的联系大于区别,区别的根本在于两个领域尝试解决的问题是不同的. 对于计算广告来说,本质上要处理的是三方利益的协调问题,这三方分别是广告主.用户和媒体. 对于推 ... 2022年第36届人工智能顶级会议AAAI论文列表已经放出,此次会议共收到9251篇论文提交,其中9020篇论文被审稿.最终录取篇数为1349篇,录取率为可怜的15%.由于境外疫情形势依然严峻,大会将 ... 猜你喜欢 0.2021年轻人性生活调研报告 1.如何搭建一套个性化推荐系统? 2.从零开始搭建创业公司后台技术栈 3.某视频APP推荐详解(万字长文) 4.微博推荐算法实践与机器学习平台演进 5.腾讯 ... 第31届信息和知识管理国际会议CIKM2022将于2022年10月17日-21日以混合会议的方式在美国亚特兰大举行.CIKM会议是数据库/数据挖掘/内容检索领域顶级国际会议,也是中国计算机学会规定的C ... 嘿,记得给"机器学习与推荐算法"添加星标 2022年第36届人工智能顶级会议AAAI论文列表已经放出,此次会议共收到9251篇论文提交,其中9020篇论文被审稿.最终录取篇数为13 ...WWW2022推荐系统/计算广告论文集锦相关推荐
最新文章
热门文章