KDD 2022 搜广推论文合集 - 文章 - 开发者社区

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KDD 2022 搜广推论文他lei了，许多大厂和名校都有上榜，包括阿里，百度，美团，腾讯，谷歌，品趣等等，这里大致总结分类了一下，一些文章可能属于多个类别，但是归到了其中一类，大家可以根据关键词搜索。大致分了以下几类：图学习，因果推荐，去偏，强化学习等等，值得注意的是

因果推断和强化学习

的论文有所增加，这也说明这两个方向的方法在推荐系统有越来越多的落地和研究了，

图学习

依旧是热点。

官网地址：https://kdd.org/kdd2022/index.html

点击率CTR

Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction【

深度点击率预测的对抗梯度驱动探索

】

Kailun Wu (Alibaba Group)*; Weijie Bian (Alibaba Group); Zhangming Chan (Alibaba Group); Lejian Ren (Alibaba Group); SHIMING XIANG (Chinese Academy of Sciences, China); Shu-Guang Han (Alibaba Group); Hongbo Deng (Alibaba Group); Bo Zheng (Alibaba Group)

A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction【

用于去偏点击后转化率预测的广义双鲁棒学习框架

】

Quanyu Dai (Huawei Noah's Ark Lab)*; Peng Wu (Peking University); Haoxuan Li (Peking University); Zhenhua Dong (Huawei Noah's Ark Lab); Xiao-Hua Zhou (Peking University); Rui Zhang (ruizhang.info); Rui zhang (Huawei Technologies Co., Ltd.); Jie Sun (Theory Lab, Huawei Hong Kong Research Center)

EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search【

EXTR：电子商务赞助搜索中具有外部性的点击率预测

】

Chi Chen (Alibaba)*; Hui Chen (Tsinghua University); Kangzhi Zhao (Alibaba Group); Junsheng Zhou (Alibaba Group); Li He (Alibaba Group); Hongbo Deng (Alibaba Group); Jian Xu (Alibaba Group); Bo Zheng (Alibaba Group); Yong Zhang (" Tsinghua University, China"); Chunxiao Xing (Tsinghua University)

Learning Supplementary NLP features for CTR Prediction in Sponsored Search【

为赞助搜索中的 CTR 预测学习补充 NLP 特征

】

Dong Wang (Microsoft)*; Shaoguang Yan (Microsoft); Yunqing Xia (Microsoft); Kave Salamatian (University of Savoie); Weiwei Deng (Microsoft); Qi Zhang (Microsoft )

Combo-Fashion: Fashion Clothes Matching CTR Prediction with Item History【

Combo-Fashion：时尚服装推荐

】

Chenxu Zhu (Shanghai Jiao Tong University)*; Peng Du (Alibaba); Weinan Zhang (Shanghai Jiao Tong University); Yong Yu (Shanghai Jiao Tong University); Yang Cao (Vision & Beauty Team, Alibaba Group)

图学习

Graph-Flashback Network for Next Location Recommendation【图学习，位置推荐】

Xuan Rao (University of Electronic Science and Technology of China)*; Lisi Chen (KAUST); Yong Liu (Nanyang Technological University); Shuo Shang (KAUST); Bin Yao (Shanghai Jiao Tong University); Peng Han (KAUST)

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation【

Top-K推荐的多方面量化强化学习二值化图表征

】

Yankai Chen (The Chinese University of Hong Kong)*; Huifeng Guo (Huawei Noah's Ark Lab); Yingxue Zhang (Huawei Technologies Canada); Chen Ma (City University of Hong Kong); Ruiming Tang (Huawei Noah's Ark Lab); Jingjie Li (Huawei Noah's Ark Lab); Irwin King (The Chinese University of Hong Kong)

User-Event Graph Embedding Learning for Context-Aware Recommendation【上下文感知的图embedding学习】

Dugang Liu (Shenzhen University)*; Mingkai He (Shenzhen University); Jinwei Luo (Shenzhen University); Jiangxu Lin (Southeast University); Meng Wang (Southeast University); Xiaolian Zhang (Huawei 2012 lab); Weike Pan (Shenzhen University); Zhong Ming (Shenzhen University)

Multi-Behavior Hypergraph-Enhanced Transformer for Next-Item Recommendation【多行为超图Transformer进行推荐】

Yuhao Yang (Wuhan University); Chao Huang (University of Hong Kong)*; Lianghao Xia (South China University of Technology); Yuxuan Liang (National University of Singapore); Yanwei Yu (Ocean University of China); Chenliang Li (Wuhan University)

Self-Augmented Hypergraph Transformer for Recommender Systems【自增强超图Transformer】

Lianghao Xia (South China University of Technology); Chao Huang (University of Hong Kong)*; Chuxu Zhang (Brandeis University)

CoRGi: Content-Rich Graph Neural Networks with Attention【

CoRGi：具有注意力的内容丰富的图神经网络

】

Jooyeon Kim (RIKEN)*; Angus Lamb (Microsoft); Simon Woodhead (Eedi); Simon Pyton Jones (Microsoft); Cheng Zhang (Microsoft); Miltiadis Allamanis (MSR Cambridge)

Friend Recommendations with Self-Rescaling Graph Neural Networks【

缩放图神经网络的朋友推荐

】

Xiran Song (Huazhong University of Science and Technology); Jianxun Lian (MSRA); Hong Huang (Huazhong University of Science and Technology)*; Mingqi Wu (Microsoft Gaming, Redmond); Hai Jin (Huazhong University of Science and Technology); Xing Xie (Microsoft Research Asia)

因果推断

ASPIRE: Air Shipping Recommendation for E-commerce Products via Causal Inference Framework【

ASPIRE：通过因果推理框架为电子商务产品提供航空运输建议

】

Abhirup Mondal (Amazon)*; Anirban Majumder (Amazon); Vineet Chaoji (Amazon)

CausalInt: Causal Inspired Intervention for Multi-Domain Recommendation【

CausalInt：多领域推荐的因果启发式干预

】

Yichao Wang (Huawei Noah's Ark Lab)*; Huifeng Guo (Huawei Noah's Ark Lab); Bo Chen (Huawei Noah’s Ark Lab); Weiwen Liu (Huawei Noah's Ark Lab); Zhirong Liu (Huawei Noah's Ark Lab); Qi Zhang (Huawei Noah's Ark Lab); Zhicheng He (Huawei Noah's Ark Lab); Hongkun Zheng (Huawei Technologies Co Ltd); Weiwei Yao (Huawei); Muyu Zhang (Huawei); Zhenhua Dong (Huawei Noah's Ark Lab); Ruiming Tang (Huawei Noah's Ark Lab)

Practical Counterfactual Policy Learning for Top- $K K$ Recommendations【

Top-K推荐的反事实策略学习

】

Yaxu Liu (National Taiwan University)*; Jui-Nan Yen (National Taiwan University); Bowen Yuan (National Taiwan University); Rundong Shi (Meituan); Peng Yan (Meituan); Chih-Jen Lin (National Taiwan University)

Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis【推荐系统中的混杂因子分析】

Sihao Ding (University of Science and Technology of China)*; Peng Wu (Peking University); Fuli Feng (University of Science and Technology of China); Yitong Wang (University of Science and Technology of China); Xiangnan He (University of Science and Technology of China); Yong Liao (University of Sciences and Technology of China); Yongdong Zhang (University of Science and Technology of China)

Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation【

视频推荐观看时间预测中的去混杂持续时间偏差

】

Ruohan Zhan (Stanford University)*; Changhua Pei (Alibaba Group); Qiang Su (Kuaishou Technology); Jianfeng WEN (Kuaishou Inc.); Xueliang Wang (University of Science and Technology of China); Guanyu Mu (Kuaishou Inc.); Dong Zheng (Kuaishou Technology); Peng Jiang (Kuaishou Inc.); Kun Gai (AI)

去偏

Debiasing Learning for Membership Inference Attacks Against Recommender Systems【

针对推荐系统的成员推理攻击的去偏学习

】

Zihan Wang (Shandong University)*; Na Huang (Shandong University); Fei Sun (Alibaba Group); Pengjie Ren (Shandong University); Zhumin Chen (Shandong University); Hengliang Luo (Meituan); Maarten de Rijke (University of Amsterdam); Zhaochun Ren (Shandong University)

Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers【使用双向Transformer在序列推荐中纠偏】

Khalil Damak (University of Louisville)*; Sami Khenissi (University of Louisville); Olfa Nasraoui (university of Louisville)

Invariant Preference Learning for General Debiasing in Recommendation【纠偏】

Zimu Wang (Tsinghua University)*; Yue He (Tsinghua University); Jiashuo Liu (Tsinghua University); Wenchao Zou (Siemens China); Philip S Yu (UNIVERSITY OF ILLINOIS AT CHICAGO); Peng Cui (Tsinghua University)

Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification【

通过奖励修改抵消音乐流推荐中的用户注意力偏差

】

Xiao Zhang (Renmin University of China ); Sunhao Dai (Renmin University of China); Jun Xu (Renmin University of China)*; Zhenhua Dong (Huawei Noah's Ark Lab); Quanyu Dai (Huawei Noah's Ark Lab); Ji-Rong Wen (Renmin University of China)

协同过滤

Towards Representation Alignment and Uniformity in Collaborative Filtering【

协同过滤中的表征对齐和均匀性

】

Chenyang Wang (Tsinghua University)*; Yuanqing Yu (Tsinghua University); Weizhi Ma (Tsinghua University); Min Zhang (Tsinghua University); Chong Chen (Tsinghua University); Yiqun LIU (Tsinghua University); Shaoping Ma (Tsinghua University)

HICF: Hyperbolic Informative Collaborative Filtering【

HICF：双曲信息协同过滤

】

Menglin Yang (The Chinese University of Hong Kong)*; Li Zhihao (Harbin Institute of Technology, Shenzhen); Min Zhou (Huawei Technologies co. ltd); Jiahong Liu (Harbin Institute of Technology(Shenzhen)); Irwin King (The Chinese University of Hong Kong)

Device-Cloud Collaborative Recommendation via Meta Controller【

元控制器端云协同推荐

】

Jiangchao Yao (Shanghai Jiao Tong University)*; Feng 8 Wang (Alibaba Group); XICHEN DING (Ant Group); SHAOHU CHEN (Ant Group); Bo Han (HKBU / RIKEN); Jingren Zhou (Alibaba Group); Hongxia Yang (Alibaba Group)

强化学习

Learning Relevant Information in Conversational Search and Recommendation using Deep Reinforcement Learning【

使用深度强化学习在会话搜索和推荐中学习相关信息

】

Ali Montazeralghaem (University of Massachusetts Amherst)*; James Allan (University of Massachusetts Amherst)

MDP2 Forest: A Constrained Continuous Multi-dimensional Policy Optimization Approach for Short-video Recommendation【

MDP2 Forest：一种用于短视频推荐的受限连续多维策略优化方法

】

Sizhe Yu (Shanghai University of Finance and Economics)*; Ziyi Liu (School of Statistics, Renmin University of China); Shixiang Wan (Tencent); zero Jay (Tencent); Zang Li (DiDi AI Labs, Didi Chuxing); Fan Zhou (Shanghai University of Finance and Economics

Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace【强化学习用于调度算法】

Soheil Sadeghi Eshkevari (DiDi Labs)*; Xiaocheng Tang (DiDi AI Labs); Zhiwei Qin (DiDi AI Labs); Jinhan Mei (DiDi Global); Cheng Zhang (Didi Chuxing); Qianying Meng (DiDi Global); Jia Xu (DiDi Global)

ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning【

贝叶斯强化学习进行 ROI 约束的投标

】

Haozhe Wang (ShanghaiTech University)*; Chao Du (Alibaba Group); Panyan Fang (Alibaba Group); Shuo Yuan (Alibaba Group); Xuming He (ShanghaiTech University); Liang Wang (Alibaba group); Bo Zheng (Alibaba Group)

Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems【

通过强化学习进行多任务融合以提高推荐系统中的长期用户满意度

】

Qihua Zhang (Tencent); Junning Liu (Tencent Inc.); Yuzhuo Dai (Tencent); Kunlun Zheng (Tencent); Fan Huang (Tencent)*; Yifan Yuan (Tencent); Xianfeng Tan (Tencent); Yiyan Qi (Tencent)

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation【

Top-K推荐的多方面量化强化学习二值化图表征

】

对比学习

User-tag Profile Modeling in Recommendation System via Contrast Weighted Tag Masking【

基于对比度加权标签掩蔽的推荐系统中的用户标签配置文件建模

】

Chenxu Zhu (Shanghai Jiao Tong University)*; Peng Du (Alibaba); Xianghui Zhu (SJTU); Weinan Zhang (Shanghai Jiao Tong University); Yong Yu (Shanghai Jiao Tong University); Yang Cao (Vision & Beauty Team, Alibaba Group)

CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation【捆绑推荐中的对比学习】

Yunshan Ma (National University of Singapore )*; Yingzhi He (National University of Singapore); An Zhang (National University of Singapore); Xiang Wang (National University of Singapore); Tat-Seng Chua (National university of Singapore)

Contrastive Cross-domain Recommendation in Matching【

匹配中的对比跨域推荐

】

Ruobing Xie (WeChat Search Application Department, Tencent)*; Qi Liu (Tencent); Liangdong Wang (WeChat, Tencent); Shukai Liu (Tencent); Bo Zhang (WeChat Search Application Department, Tencent); Leyu Lin (WeChat Search Application Department, Tencent)

表征学习

Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation【

具有分离表征的特征感知多样化重排，用于相关推荐

】

Zihan Lin (Renmin University of China)*; Hui Wang (Renmin University of China); Jingshu Mao (Kuaishou Inc); Wayne Xin Zhao (Renmin University of China); Cheng Wang (Kuaishou Inc); Peng Jiang (Kuaishou Inc.); Ji-Rong Wen (Renmin University of China)

ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest【

ItemSage：在 Pinterest 学习用于购物推荐的产品embedding

】

Paul D Baltescu (Pinterest)*; Paul Baltescu (Pinterest); Haoyu Chen (Pinterest); Nikil Pancha (Pinterest, Inc.); Andrew H Zhai (Pinterest, Inc.); Jure Leskovec (Stanford University); Charles Rosenberg (Pinterest)

PinnerFormer: Sequence Modeling for User Representation at Pinterest【

PinnerFormer：Pinterest 用户表征的序列建模

】

Nikil Pancha (Pinterest, Inc.)*; Andrew H Zhai (Pinterest, Inc.); Jure Leskovec (Stanford University); Charles Rosenberg (Pinterest)

Recommendation in offline stores: A gamification approach for learning the spatiotemporal representation of indoor shopping【

线下商店推荐：一种学习室内购物时空表征的游戏化方法

】

JongKyung Shin (Ulsan National Institute of Science and Technology); Changhun Lee (UNIST); Chiehyeon Lim (Ulsan National Institute of Science and Technology)*; Yunmo Shin (Retailtech co., Ltd.); Junseok Lim (Retailtech co., Ltd.)

Towards Universal Sequence Representation Learning for Recommender Systems【

面向推荐系统的通用序列表征学习

】

Yupeng Hou (Renmin University of China)*; Shanlei Mu (Renmin University of China); Wayne Xin Zhao (Renmin University of China); Yaliang Li (Alibaba Group); Bolin Ding ("Data Analytics and Intelligence Lab, Alibaba Group"); Ji-Rong Wen (Renmin University of China)

Aligning Dual Disentangled User Representations from Ratings and Textual Content【

从评级和文本内容中对齐双重分离的用户表征

】

Nhu-Thuat Tran (Singapore Management University)*; Hady Lauw (Singapore Management University)

特征交互与选择

Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems【有益特征交互】

Yixin Su (The University of Melbourne)*; Yunxiang Zhao (The University of Melbourne); Sarah Erfani (University of Melbourne); Junhao Gan (University of Melbourne); Rui Zhang (ruizhang.info)

Adaptive Feature Selection in Deep Recommender Systems【

深度推荐系统中的自适应特征选择

】

Weilin LIN (City University of HongKong); Xiangyu Zhao (City University of Hong Kong)*; Yejing Wang (City University of Hongkong); Tong Xu (University of Science and Technology of China); Xian Wu (Tencent Medical AI Lab)

公平性，冷启动，个性化，多样性

Comprehensive Fair Meta-learned Recommender System【公平性，元学习】

Tianxin Wei (University of Illinois Urbana Champaign)*; Jingrui He (University of Illinois at Urbana-Champaign)

Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations【

基于移动应用使用情况的任务优化用户聚类用于冷启动推荐

】

Bulou Liu (Tsinghua University)*; Bing Bai (Tencent); Weibang Xie (Tencent); Yiwen Guo (Independent Researcher); Hao Chen (UC Davis)

Personalized Chit-Chat Generation for Recommendation Using External Chat Corpora【

使用外部聊天语料库进行推荐的个性化闲聊生成

】

Changyu Chen (Renmin University of China); Xiting Wang (Microsoft Research Asia)*; Xiaoyuan Yi (Microsoft Research Asia); Fangzhao Wu (MSRA); Xing Xie (Microsoft Research Asia); Rui Yan (Peking University)

PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions【个性化注意力融合的序列推荐】

Ehsan Gholami (University of California, Davis)*; Mohammad Motamedi (University of California, Davis); Ashwin Aravindakshan (University of California-Davis)

Uncovering the Heterogeneous Effects of Preference Diversity on User Activeness: A Dynamic Mixture Model【

揭示偏好多样性对用户活跃度的异质影响：动态混合模型

】

Yunfei Lu (Huawei)*; Peng Cui (Tsinghua University); Linyun Yu (Bytedance AI Lab); Lei Li (Bytedance); Wenwu Zhu (Tsinghua University)

硬件，系统等

Lion: A GPU-Accelerated Online Serving System for Web-Scale Recommendation at Baidu【GPU加速】

Hao Liu (HKUST)*; Qian Gao (Baidu, Inc); Xiaochao Liao (Baidu Inc.); Guangxing Chen (Baidu, Inc.); Hao Xiong (Baidu, Inc.); Silin Ren (Baidu Inc.); Guobao Yang (Baidu, Inc.); Zhiwei Zha (Baidu, Inc.)

Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters【

Persia：一个开放的混合系统，将基于深度学习的推荐系统扩展到 100 万亿个参数

】

Xiangru Lian (University of Rochester)*; Binhang Yuan (ETH Zurich); Xuefeng Zhu (Kuaishou Technology); Yulong Wang (Kuaishou Technology); Yongjun He (ETH Zürich); wu honghuan (Kuaishou); Lei Sun (Kuaishou Technology); Haodong Lyu (Kuaishou Technology); Chengjun Liu (Kuaishou Technology); Xing Dong (Kuaishou Technology); Yiqiao Liao (Kuaishou Technology); Mingnan Luo (Kuaishou Technology); Congfei Zhang (Kuaishou Technology); Jingru Xie (Kwai Inc.); Haonan Li (Kuaishou Technology); Lei Chen (Kuaishou Technology); Renjie Huang (Kuaishou Technology); Jianying Lin (Kuaishou); Chengchun Shu (Kuaishou Technology); Xuezhong Qiu (Kuaishou Technology); Zhishan Liu (Kuaishou Technology); Dongying Kong (Kuaishou Technology); Lei Yuan (Kuaishou Technology); Hai Yu (Kuaishou Technology); Sen Yang (Kwai Inc.); Ce Zhang (ETH); Ji Liu (Kwai Inc.)

AutoShard: Automated Embedding Table Sharding for Recommender Systems【

AutoShard：推荐系统的自动嵌入表分片

】

Daochen Zha (Rice University)*; Louis Feng (Meta); Bhargav Bhushanam (Facebook); Dhruv Choudhary (Facebook Inc.); Jade Nie (Meta); Yuandong Tian (Facebook); Jay Chae (Meta); Yinbin Ma (Meta Platforms, Inc.); Arun Kejariwal (Facebook Inc.); Xia Hu (Rice University)

其他

Knowledge-enhanced Black-box Attacks for Recommendations【

针对推荐的知识增强黑盒攻击

】

Jingfan Chen (Nanjing University)*; Wenqi FAN (The Hong Kong Polytechnic University); Guanghui Zhu (Nanjing University); Xiangyu Zhao (City University of Hong Kong); Chunfeng Yuan (Nanjing University); Qing Li (The Hong Kong Polytechnic University ); Yihua Huang (Nanjing University)

FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling【联邦推荐】

Chuhan Wu (Tsinghua University)*; Fangzhao Wu (MSRA); Tao Qi (Tsinghua University); Yongfeng Huang (Tsinghua University); Xing Xie (Microsoft Research Asia)

Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning【

通过知识增强提示学习实现统一的会话推荐系统

】

Xiaolei Wang (Renmin University of China); Kun Zhou (Renmin University of China); Ji-Rong Wen (Renmin University of China); Wayne Xin Zhao (Renmin University of China)*

Affective Signals in a Social Media Recommender System【

社交媒体推荐系统中的情感信号

】

Jane Yu (Facebook)*; Yi-Chia Wang (Uber); Lijing Qin (Meta AI); Canton Cristian (Facebook AI); Alon Y Halevy (Facebook)

Pricing the Long Tail by Explainable Product Aggregation and Monotonic Bandits【

通过可解释的产品聚合和单调Bandit为长尾定价

】

Marco Mussi (Politecnico di Milano)*; Gianmarco Genalti (Politecnico di Milano); Francesco Trovò (Politecnico di Milano); Alessandro Nuara (Politecnico di Milano, Italy); Nicola Gatti (Politecnico di Milano); Marcello Restelli (Politecnico di Milano)

Multi Armed Bandit vs. A/B Tests in E-commence - Confidence Interval and Hypothesis Test Power Perspectives【

电子商务中的MAB与 A/B 测试 - 置信区间和假设测试功率观点

】

Ding Xiang (The Home Depot)*; Rebecca West (The Home Depot); Jiaqi Wang (The Home Depot); Xiquan Cui (Homedepot); Jinzhou Huang (The Home Depot)

Modeling the Effect of Persuasion Factor on User Decision for Recommendation【

对说服因素对用户推荐决策的影响进行建模

】

Chang Liu (Tsinghua University)*; Chen Gao (Tsinghua University); Yuan Yuan (Tsinghua University); BAI CHEN (Meituan); Lingrui Luo (Meituan); Xiaoyi Du (Meituan); shi xinlei (meituan); Hengliang Luo (Meituan); Depeng Jin (Tsinghua University); Yong Li (Tsinghua University)

TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation【

TwHIN：异构信息网络实现个性化推荐

】

Ahmed El-Kishky (Twitter)*; Thomas Markovich (Twitter); Serim Park (Twitter); Chetan Verma (Twitter); Baekjin Kim (Twitter); Ramy Eskander (Twitter); Yury Malkov (Twitter); Frank Portman (Twitter); Sofia Samaniego (Twitter); Ying Xiao (Twitter); Aria Haghighi (Twitter)

4SDrug: Symptom-based Set-to-set Small and Safe Drug Recommendation【

4SDrug：基于症状的套装小而安全的药物推荐

】

Yanchao Tan (Zhejiang University)*; Chengjun Kong (National University of Singapore); Leisheng Yu (Emory University); Pan Li (Purdue University); Chaochao Chen (Zhejiang University); Xiaolin Zheng (Zhejiang University); Vicki Hertzberg (Emory University); Carl Yang (Emory University)

Surrogate for Long-Term User Experience in Recommender Systems【

推荐系统中长期用户体验的替代品

】

Yuyan Wang (Google Brain)*; Mohit Sharma (University of Minnesota); Sriraj Badam (Google); Can Xu (Google); Qian Sun (Google); Lee Richardson (Google); Lisa Chung (Google); Ed H. Chi (Google); Minmin Chen (Google)

Automatically Discovering User Consumption Intents in Meituan【

美团自动发现用户消费意图

】

Yinfeng Li (Tsinghua University)*; Chen Gao (Tsinghua University); Xiaoyi Du (Meituan); HUAZHOU WEI (Meituan); Hengliang Luo (Meituan); Depeng Jin (Tsinghua University); Yong Li (Tsinghua University)

NxtPost: User to Post Recommendations in Facebook Groups【

NxtPost：Facebook 群组中的推荐

】

Fedor Borisyuk (Facebook)*; Kaushik Rangadurai (Facebook); Yiqun Liu (Facebook); Siddarth Malreddy (Facebook); Xiaoyi Liu (Facebook)】

Automatic Generation of Product-Image Sequence in E-commerce【

电子商务中产品-图像序列的自动生成

】

Xiaochuan Fan (JD.com)*; Chi Zhang (JD.com); Yong Yang (JD); Yue Shang (JD.com); xueying zhang (jd.com silicon valley research center); Zhen He (JD); Xiao Yun (JD.com); Bo Long (JD.com); Lingfei Wu (JD.COM Silicon Valley Research Center)

An Online Multi-task Learning Framework for Google Feed Ads Auction Models【

适用于 Google Feed 广告拍卖模型的在线多任务学习框架

】

Ning Ma (Google)*; Mustafa Ispir (Google); Yuan Li (Google); Yongpeng Yang (Google); Zhe Chen (Google); Derek Zhiyuan Cheng (Google); Lan Nie (Google); Kishor Barman (Google)

CERAM: Coverage Expansion for Recommendations by Associating Discarded Models【

CERAM：通过关联废弃模型来扩展推荐的覆盖范围

】

Yoshiki Matsune (Ritsumeikan University)*; Kota Tsubouchi (Yahoo Japan Corporation); Nobuhiko Nishio (Ritsumeikan University)

Training Large-Scale News Recommenders with Pretrained Language Models in the Loop【

在循环中使用预训练的语言模型训练大规模新闻推荐

】

Shitao Xiao (BUPT)*; Zheng Liu (MSRA); Yingxia Shao (BUPT); Tao Di (microsoft); Fangzhao Wu (Microsoft Research Asia); Bhuvan Middha (Microsoft); Xing Xie (Microsoft Research Asia)

Intelligent Request Strategy Design in Recommender System【

推荐系统中的智能请求策略设计

】

Xufeng Qian (Alibaba Group)*; Yue Xu (Alibaba Group); Fuyu Lv (Alibaba Group); Shengyu Zhang (Zhejiang University); Ziwen Jiang (Alibaba); Qingwen Liu (Alibaba Group ); Xiaoyi Zeng (Alibaba Group); Tat-Seng Chua (National university of Singapore); Fei Wu (Zhejiang University, China)

DDR: Dialogue based Doctor Recommendation for Online Medical Service【

DDR：基于对话的在线医疗服务医生推荐

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Zhi Zheng (University of Science and Technology of China)*; Zhaopeng Qiu (Tencent Medical AI Lab); Tong Xu (University of Science and Technology of China); Xian Wu (Tencent Medical AI Lab); Xiangyu Zhao (City University of Hong Kong); Enhong Chen (University of Science and Technology of China); Hui Xiong (Rutgers University)

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