会员服务 ·

WWW2022推荐系统/计算广告论文集锦

2022 年 3 月 2 日 机器学习与推荐算法

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