KDD2020

Graph-based Recommendation（基于图，不是知识图谱）

1. A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks 【华为诺亚】

2. An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph 【Amazon】

论文：arxiv.org/abs/2007.0021

3. M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems 【阿里】

简介：本文通过关联多个视角的图(item-item图、item-shop图、shop-shop图等)增强item表征，用于item召回。

论文：arxiv.org/abs/2005.1011

4. Handling Information Loss of Graph Neural Networks for Session-based Recommendation

5. Interactive Path Reasoning on Graph for Conversational Recommendation

论文：arxiv.org/abs/2007.0019

6. A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce 【阿里】

7. Gemini: A Novel and Universal Heterogeneous Graph Information Fusing Framework for Online Recommendations 【滴滴】

CIKM2020

CIKM2020(http://www.cikm2020.org/)是数据挖掘相关领域一大盛会，将于10月召开，相关论文列表已经放出。下面对本次接收的推荐系统论文进行了筛选和整理。按照推荐系统中的应用场景可以大致划分为：CTR预估、序列推荐、文本类推荐、Job推荐、社交推荐、Bundle推荐等。同时，GNN、知识图谱、知识蒸馏、强化学习、迁移学习、AutoML在推荐系统的落地应用也成为当下的主要研究点。从工业界参会来看，CIKM2020明显不如KDD2020，主要集中在国内大厂包括阿里、华为、百度、平安等，国外厂商少见。
https://www.136.la/jingpin/show-88484.html

Knowledge Graph-enhanced Recommendation

News Recommendation with Topic-Enriched Knowledge Graphs
Multi-modal Knowledge Graphs for Recommender Systems

论文：zheng-kai.com/paper/cik

CAFE: Coarse-to-Fine Knowledge Graph Reasoning for E-Commerce Recommendation
MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings

论文：

https://people.cs.aau.dk/~matteo/publications…

WWW2021知识图谱推荐系统相关的论文

知识图谱

Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection 适应自然对话中的上下文知识以实现多轮反应的选择
An Adversarial Transfer Network for Knowledge Representation Learning 用于知识表示学习的对抗性转移网络
Boosting the Speed of Entity Alignment 10×: Dual Attention Matching Network with Normalized Hard Sample Mining 将实体对齐的速度提高10倍: 带有归一化硬样本挖掘的双注意匹配网络
DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge 弥合话语知识与常识性知识之间的差距
Effective and Scalable Clustering on Massive Attributed Graphs 大规模归属图上有效且可扩展的聚类方法
Efficient Computation of Semantically Cohesive Subgraphs for Keyword-Based Knowledge Graph Exploration 基于关键词的知识图谱探索的语义内聚子图的高效计算
Efficient Knowledge Graph Embedding without Negative Sampling 无负抽样的高效知识图谱嵌入
Enquire One’s Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion 决策前查询自己的父母和子女: 充分利用层次结构进行自我监督的分类法扩展
Few-Shot Knowledge Validation using Rules 使用规则的少量知识验证
Inductive Entity Representations from Text via Link Prediction 通过链接预测从文本中归纳出实体代表
Information Extraction From Co-Occurring Similar Entities 从共存的相似实体中提取信息
Knowledge Embedding Based Graph Convolutional Network 基于图卷积网络的知识嵌入
Learning Intents behind Interactions with Knowledge Graph for Recommendation 用知识图谱学习互动背后的意图以进行推荐
MedPath: Augmenting Health Risk Prediction via Medical Knowledge Paths 通过医学知识路径增强健康风险预测的能力
Mixed-Curvature Multi-relational Graph Neural Network for Knowledge Graph Completion 用于知识图谱完成的混合曲率多关系图谱神经网络
MQuadE: a Unified Model for Knowledge Fact Embedding 一个知识事实嵌入的统一模型
MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings 低维知识图谱嵌入的多教师知识蒸馏法
OntoZSL: Ontology-enhanced Zero-shot Learning 本体论增强的零点学习
Pivot-based Candidate Retrieval for Cross-lingual Entity Linking 基于枢轴的候选检索的跨语言实体链接
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network 在图神经网络中使用知识图谱上下文的关系提取
RETA: A Schema-Aware, End-to-End Solution for Instance Completion in Knowledge Graphs 知识图谱中实例完成的模式感知、端到端解决方案
Revisiting the Evaluation Protocol of Knowledge Graph Completion Methods for Link Prediction 重新审视链接预测的知识图谱完成方法的评估协议
Role-Aware Modeling for N-ary Relational Knowledge Bases 角色感知的N次方关系知识库建模
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs 从知识图谱的逻辑查询中获得自监督的双曲面表征
Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion 用于高效知识图谱补全的结构增强型文本表征学习
TCN: Table Convolutional Network for Web Table Interpretation 用于网络表解释的表卷积网络
Trav-SHACL: Efficiently Validating Networks of SHACL Constraints 有效地验证SHACL约束的网络
Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out 事实性知识图谱中的打字错误：严重性和可能的解决方法
WiseKG: Balanced Access to Web Knowledge Graphs 网络知识图谱的平衡访问

推荐系统

A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation 用于改进长尾物品推荐的双转移学习框架
A Multi-Agent Reinforcement Learning Framework for Intelligent Electric Vehicle Charging Recommendation 一种用于智能电动车充电推荐的多代理强化学习框架
A Recommender System for Crowdsourcing Food Rescue Platforms 众包食品救援平台的推荐系统
A Workflow Analysis of Context-driven Conversational Recommendation 语境驱动的对话式推荐的工作流程分析
Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation 顺序推荐的对抗性和对比性变异自动编码器
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start 对抗式式项目推广：利用图像解决冷启动的Top-N推荐器的核心漏洞
Bidirectional Distillation for Top-K Recommender System 双向蒸馏法的Top-K推荐系统
Collaborative Filtering with Preferences Inferred from Brain Signals 利用大脑信号推断的偏好进行协同过滤
ConceptGuide: Supporting Online Video Learning with Concept Map-based Recommendation of Learning Path 概念指南：基于概念图的学习路径推荐，支持在线视频学习
Cost-Effective and Interpretable Job Skill Recommendation with Deep Reinforcement Learning 深度强化学习的成本效益和可解释的工作技能推荐
Debiasing Career Recommendations with Neural Fair Collaborative Filtering 用神经公平协作过滤法进行职业的无偏推荐
DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce 移动商务中用于保护隐私的顺序推荐的设备上深度学习
Disentangling User Interest and Conformity for Recommendation with Causal Embedding 用因果嵌入的方式区分用户兴趣和符合性的推荐
Diversified Recommendation Through Similarity-Guided Graph Neural Networks 通过相似性引导的图谱神经网络实现多样化推荐
Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective 移动中的多样性! 最大诱导卡度目标下的流决定性的点过程
Drug Package Recommendation via Interaction-aware Graph Induction 通过交互感知图诱导的药物包推荐
Dual Side Deep Context-aware Modulation for Social Recommendation 面向社会推荐的双侧深度语境感知调控
ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models 从用户对解释的反馈中学习以改进推荐模型
Field-aware Embedding Space Searching in Recommender Systems 推荐系统中的场感知嵌入空间搜索
FINN: Feedback Interactive Neural Network for Intent Recommendation 用于意向性推荐的反馈交互式神经网络
Future-Aware Diverse Trends Framework for Recommendation 适应未来的多样化趋势的推荐框架
Graph Embedding for Recommendation against Attribute Inference Attacks 针对属性推理攻击的图式嵌入推荐
HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering HGCF: 协同过滤的双曲图卷积网络
High-dimensional Sparse Embeddings for Collaborative Filtering 协同过滤的高维稀疏嵌入技术
Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching 用于在线查询-POI匹配的增量时空图学习
Interest-aware Message-Passing GCN for Recommendation 用于推荐的兴趣感知信息传递GCN
Large-scale Comb-K Recommendation 大规模Comb-K推荐
Learning Fair Representations for Recommendation: A Graph-based Perspective 基于图的视角为推荐学习公平表征
Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation 学习用户偏好的异质性时间模式以实现及时推荐
Learning Intents behind Interactions with Knowledge Graph for Recommendation 学习与知识图谱互动背后的意图以进行推荐
Leveraging Review Properties for Effective Recommendation 利用评论属性进行有效推荐
Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation 利用编码直方图的线性时间自我关注实现高效推荐
Neural Collaborative Reasoning 协同推理神经系统
Personalized Approximate Pareto-Efficient Recommendation 个性化近似帕累托效率的推荐
Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests 兴趣不断变化的分布感知型推荐
Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks 擦除的随机漫步：社会和信息网络上多样化的个性化推荐
Reinforcement Recommendation with User \Multi-aspect Preference 用户多面性偏好的强化推荐
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation 用于整体顺序推荐的关系性时空注意图神经网络
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation 用于社会推荐的自我监督的多通道超图卷积网络
Session-aware Linear Item-Item Models for Session-based Recommendation 基于会话感知的线性项目-项目模型的推荐
Sinkhorn Collaborative Filtering Sinkhorn协同过滤
STAN: Spatio-Temporal Attention Network for next Point-of-Interest Recommendation 用于下一个兴趣点推荐的空间-时间注意网络
Task-adaptive Neural Process for User Cold-Start Recommendation 用于用户冷启动推荐的任务适应性神经过程
The Interaction between Political Typology and Filter Bubbles in News Recommendation Algorithms 新闻推荐算法中政治类型学与过滤气泡的相互作用
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities 迈向内容提供商感知的推荐系统：关于用户和供应商效用之间相互作用的模拟研究
User Simulation via Supervised Generative Adversarial Network 通过有监督的生成对抗网络进行用户模拟
User-oriented Group Fairness In Recommender Systems 推荐系统中面向用户的群体公平性
Variation Control and Evaluation for Generative Slate Recommendations 生成式板块推荐的变异控制与评估
Where Next? A Dynamic Model of User Preferences 下一步是什么？用户偏好的动态模型

SIGIR2021

KDD2021

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篇论文被接收，接收率为15.44%，相比KDD2020的接收率16.8%有所下降。其中，涉及到的推荐系统相关的论文共38篇（包括Research Track和Applied Data Science Track），相比于去年的32篇有所增加KDD2020推荐系统论文聚焦（注：本文涉及的推荐系统相关论文的整理很可能具有极强的个人倾向，因此请勿抬杠。另外，整理不易，欢迎小手点个在看/分享）。

本公众号一如既往的收集与整理了发表在该会议上的推荐系统相关论文，以供研究者与工程师们提前一睹为快。本会议接受的论文主要分为了Research Track Papers与Applied Data Science Track Papers，因此大家可以即关注学术界的最新动态，也可以学习业界贴合真实场景的技巧。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，官网接收论文列表如下：

https://kdd.org/kdd2021/accepted-papers/index

Research Track Papers

研究赛道的论文主要按照推荐子领域来划分，比如推荐系统中的隐私与安全、推荐系统中的偏置、推荐系统与边缘计算结合、基于自监督的推荐系统、基于只是蒸馏的推荐系统、冷启动问题、协同过滤问题、推荐效率问题等！
[1] Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data

Authors: Hengtong Zhang (University at Buffalo)*; Changxin Tian
(Renmin University of China); Yaliang Li (Alibaba Group); Lu Su (SUNY
Buffalo); Jing Gao (University at Buffalo); Nan Yang (The school of
Information, Renmin University of China); Wayne Xin Zhao (Renmin
University of China)

[2] Deconfounded Recommendation for Alleviating Bias Amplification

Authors: Wenjie Wang (National University of Singapore)*; Fuli Feng
(National University of Singapore); Xiangnan He (University of Science
and Technology of China); Xiang Wang (National University of
Singapore); Tat-Seng Chua (National university of Singapore)

[3] Efficient Collaborative Filtering via Data Augmentation and Step-size Optimization

Authors: Xuejun Liao (SAS Institute Inc. )*; Patrick Koch (SAS
Institute Inc.); Shunping Huang (SAS Institute Inc.); Yan Xu (SAS
Institute Inc.)

[4] Efficient Data-specific Model Search for Collaborative Filtering

Authors: Chen Gao (Tsinghua University)*; Quanming Yao (4Paradigm);
Depeng Jin (Tsinghua University); Yong Li (Tsinghua University)

[5] Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems

Authors: Yinan Zhang (School of Computer Science and Engineering,
Nanyang Technological University)*; Boyang Li (Nanyang Technological
University); Yong Liu (Nanyang Technological University); Hao Wang
(Alibaba Group); Chunyan Miao (NTU)

[6] Learning Elastic Embeddings for Customizing On-Device Recommenders

Authors: Tong Chen (The University of Queensland)*; Hongzhi Yin (The
University of Queensland); Yujia Zheng (University of Electronic
Science and Technology of China); Zi Huang (University of Queensland);
Yang Wang (Hefei University of Technology); Meng Wang (Hefei
University of Technology)

[7] Learning to Embed Categorical Features without Embedding Tables for Recommendation

Authors: Wang-Cheng Kang (Google)*; Zhiyuan Cheng (Google); Tiansheng
Yao (Google); Xinyang Yi (Google); Ting Chen (Google); Lichan Hong
(Google); Ed H. Chi (Google)

[8] Learning to Recommend Visualizations from Data

Authors: Xin Qian (University of Maryland, College Park)*; Ryan A.
Rossi (Adobe Research); Fan Du (Adobe Research); Sungchul Kim (Adobe);
Eunyee Koh (Adobe); Sana Malik (Adobe); Tak Yeon Lee (Adobe Research);
Joel Chan (University of Maryland)

[9] MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems

Authors: Tinglin Huang (Zhejiang University)*; Yuxiao Dong (Facebook
AI); Ming Ding (Tsinghua University); Zhen Yang (Tsinghua University);
Wenzheng Feng (Tsinghua University); Xinyu Wang (Zhejiang University);
Jie Tang (Tsinghua University)

[10] Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

Authors: Tianxin Wei (University of Science and Technology of China)*;
Fuli Feng (National University of Singapore); Jiawei Chen (University
of Science and Technology of China); Ziwei Wu (University of Science
and Technology of China); Jinfeng Yi (JD AI Research); Xiangnan He
(University of Science and Technology of China)

[11] Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation

Authors: Jiawei Zheng (South China University of Technology); Qianli
Ma (South China University of Technology)*; Hao Gu (Tencent Technology
(SZ) Co., Ltd.); Zhenjing Zheng (South China University of Technology)

[12] Popularity Bias in Dynamic Recommendation

Authors: Ziwei Zhu (Texas A&M University)*; Yun He (Texas A&M
University); Xing Zhao (Texas A&M University); James Caverlee (Texas
A&M University)

[13] Preference Amplification in Recommender Systems

Authors: Dimitris Kalimeris (Harvard); Smriti Bhagat (Facebook)*;
Shankar Kalyanaraman (Facebook); Udi Weinsberg (Facebook)

[14] PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network

Authors: Yao Zhou (University of Illinois at Urbana-Champaign)*;
Jianpeng Xu (Walmart Labs); Jun Wu (University of Illinois at
Urbana–Champaign); Zeinab Taghavi Nasrabadi (Walmart Labs); Evren
Korpeoglu (Walmart Labs); Kannan Achan (Walmart Labs); Jingrui He
(University of Illinois at Urbana-Champaign)

[15] Socially-Aware Self-Supervised Tri-Training for Recommendation

Authors: Junliang Yu (University of Queesland); Hongzhi Yin (The
University of Queensland)*; Min Gao (Chongqing University); Xin Xia
(The University of Queensland); Xiangliang Zhang (" King Abdullah
University of Science and Technology, Saudi Arabia"); Quoc Viet Hung
Nguyen (Griffith University)

[16] Table2Charts: Recommending Charts by Learning Shared Table Representations

Authors: Mengyu Zhou (Microsoft Research)*; Qingtao Li (Peking
University); Xinyi He (Xi’an Jiaotong University); Yuejiang Li
(Tsinghua University); Yibo Liu (New York University); Wei Ji
(Microsoft); Shi Han (Microsoft Research); Yining Chen (Microsoft);
Daxin Jiang (Microsoft, Beijing, China); Dongmei Zhang (Microsoft
Research Asia)

[17] Topology Distillation for Recommender System

Authors: SeongKu Kang (POSTECH)*; Junyoung Hwang (POSTECH); Wonbin
Kweon (POSTECH); Hwanjo Yu (POSTECH)

[18] Towards a Better Understanding of Linear Models for Recommendation

Authors: Ruoming Jin (Kent State University)*; Dong Li (Kent State
University); Jing Gao (iLambda); Zhi Liu (iLambda); Li Chen (iLambda);
Yang Zhou (Auburn University)

[19] Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems

Authors: Chenwang Wu (University of Science and Technology of China)*;
Defu Lian (University of Science and Technology of China); Yong Ge
(The University of Arizona); Zhihao Zhu (University of Science and
Technology of China); Enhong Chen (University of Science and
Technology of China)

[20] Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems

Authors: Sixiao Zhang (University of Technology Sydney); Hongxu Chen
(University of Technology Sydney)*; Xiao Ming (ShanDong University);
Lizhen Cui (ShanDong University); Hongzhi Yin (The University of
Queensland); Guandong Xu (University of Technology Sydney, Australia)

Applied Data Science Track Papers

[1] A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps

Authors: Léa Briand (Deezer); Guillaume Salha-Galvan (Deezer / École
polytechnique)*; Walid Bendada (Deezer); Mathieu Morlon (Deezer);
Viet-Anh Tran (Deezer)

[2] Adversarial Feature Translation for Multi-domain Recommendation

Authors: Xiaobo Hao (WeChat Search Application Department, Tencent);
Yudan Liu (WeChat Search Application Department, Tencent); Ruobing Xie
(WeChat Search Application Department, Tencent)*; Kaikai Ge (WeChat
Search Application Department, Tencent); Linyao Tang (WeChat Search
Application Department, Tencent); Xu Zhang (WeChat Search Application
Department, Tencent); Leyu Lin (WeChat Search Application Department,
Tencent)

[3] Architecture and Operation Adaptive Network for Online Recommendations

Authors: Lang Lang (Didi Chuxing); zhenlong zhu (Didi Chuxing); Xuanye
Liu (Didi Chuxing); Jianxin Zhao (Didi Chuxing); Jixing Xu (Didi
Chuxing)*; Minghui Shan (Didi Chuxing)

[4] Automated Loss Function Search in Recommendations

Authors: Xiangyu Zhao (Michigan State University)*; Haochen Liu
(Michigan State University); Wenqi FAN (The Hong Kong Polytechnic
University); Hui Liu (Michigan State University); Jiliang Tang
(Michigan State University); Chong Wang (ByteDance)

[5] Bootstrapping Recommendations at Chrome Web Store

Authors: Zhen Qin (Google)*; Honglei Zhuang (Google Research); Rolf
Jagerman (Google Research); Xinyu Qian (Google Inc.); Po Hu (Google
Inc.); Dan Chary Chen (Google Inc.); Xuanhui Wang (Google); Michael
Bendersky (Google); Marc Najork (Google)

[6] Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

Authors: Chang Zhou (Alibaba Group); Jianxin Ma (Alibaba Group)*;
Jianwei Zhang (Alibaba Group); Jingren Zhou (Alibaba Group); Hongxia
Yang (Alibaba Group)

[7] Curriculum Meta-Learning for Next POI Recommendation

Authors: Yudong Chen (Tsinghua University)*; Xin Wang (Tsinghua
University); Miao Fan (Baidu); Jizhou Huang (Baidu); Shengwen Yang
(Baidu); Wenwu Zhu (Tsinghua University)

[8] Debiasing Learning based Cross-domain Recommendation

Authors: Siqing Li (Renmin University of China)*; Liuyi Yao (Alibaba
Group); Shanlei Mu (Renmin University of China); Wayne Xin Zhao
(Renmin University of China); Yaliang Li (Alibaba Group); Tonglei Guo
(Alibaba Group); Bolin Ding (“Data Analytics and Intelligence Lab,
Alibaba Group”); Ji-Rong Wen (Renmin University of China)

[9] Device-Cloud Collaborative Learning for Recommendation

Authors: Jiangchao Yao (Damo Academy, Alibaba Group)*; Feng Wang
(Alibaba Group); Kunyang Jia (DAMO Academy, Alibaba Group); Bo Han
(HKBU / RIKEN); Jingren Zhou (Alibaba Group); Hongxia Yang (Alibaba
Group)

[10] FleetRec: Large-Scale Recommendation Inference on Hybrid GPU-FPGA Clusters

Authors: Wenqi Jiang (ETH Zurich)*; Zhenhao He (ETH Zurich); Shuai
Zhang (ETH Zurich); Kai Zeng (Alibaba Group); Liang Feng (Alibaba
Group); Jiansong Zhang (Alibaba Group); Tongxuan Liu (Alibaba Group);
Yong Li (Alibaba Group); Jingren Zhou (Alibaba Group); Ce Zhang (ETH);
Gustavo Alonso (ETHZ)

[11] Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters

Authors: Yuzhen Huang (Facebook Inc.)*; Xiaohan Wei (Facebook); Xing
Wang (Facebook Inc.); Jiyan Yang (Facebook Inc.); Bor-Yiing Su
(Facebook); Shivam Bharuka (Facebook); Dhruv Choudhary (Facebook
Inc.); Zewei Jiang (Facebook); Hai Zheng (Facebook); Jack Langman
(Facebook)

[12] Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation

Authors: Sanshi Yu (University of Science and Technology of China);
Zhuoxuan Jiang (JD AI Research)*; Dong-Dong Chen (JD AI Research);
Shanshan Feng (Harbin Institute of Technology, Shenzhen); Dongsheng Li
(Microsoft Research Asia); Qi Liu (" University of Science and
Technology of China, China"); Jinfeng Yi (JD AI Research)

[13] Recommending the Most Effective Interventions to Improve Employment for Job Seekers with Disability

Authors: Ha Xuan TRAN (University of South Australia)*; Thuc Duy Le
(University of South Australia); Jiuyong Li (University of South
Australia); Lin Liu (University of South Australia); Jixue Liu
(University of South Australia); Yanchang Zhao (CSIRO); Tony Waters
(Maxima Training Group (Aust) Ltd.)

[14] SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations

Authors: Chenyi Lei (University of Science and Technology of China,
Alibaba Group)*; Yong Liu (Nanyang Technological University); lingzi
zhang (Nanyang Technological University); Guoxin Wang (Alibaba Group);
Haihong Tang (Alibaba Group); Houqiang Li (University of Science and
Technology of China); Chunyan Miao (NTU)

[15] Sliding Spectrum Decomposition for Diversified Recommendation

Authors: Yanhua Huang (Xiaohongshu)*; Weikun Wang (Xiaohongshu); Lei
Zhang (Xiaohongshu); Ruiwen Xu (Xiaohongshu)

[16] Towards the D-Optimal Online Experiment Design for Recommender Selection

Authors: Da Xu (Walmart Labs)*; Chuanwei Ruan (Walmart Labs); Evren
Korpeoglu (Walmart Labs); Sushant Kumar (Walmart Labs); Kannan Achan
(Walmart Labs)

[17] Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism

Authors: Vipul Gupta (UC Berkeley)*; Dhruv Choudhary (Facebook Inc.);
Peter Tang (Facebook Inc.); Xiaohan Wei (Facebook); Yuzhen Huang
(Facebook Inc.); Xing Wang (Facebook Inc.); Arun Kejariwal (Facebook
Inc.); Ramchandran Kannan (Department of Electrical Engineering and
Computer Science University of California, Berkeley); Michael Mahoney
(“University of California, Berkeley”)

[18] We Know What You Want: An Advertising Strategy Recommender System for Online Advertising

Authors: Liyi Guo (Shanghai Jiao Tong University)*; Junqi Jin (Alibaba
Group); Haoqi Zhang (Shanghai Jiao Tong University); ZHENZHE ZHENG
(Shanghai Jiao Tong University); Zhiye Yang (Alibaba Group); Zhizhuang
Xing (Alibaba Group); Fei Pan (Alibaba Group); Lvyin Niu (Alibaba
Group); FAN WU (Shanghai Jiao Tong University); Haiyang Xu (Alibaba
Group); Chuan Yu (Alibaba Group); Yuning Jiang (Alibaba Group);
Xiaoqiang Zhu (Alibaba Group)

https://github.com/hongleizhang/RSPapers（2018-2020）

Knowledge Graph for RS

Fuzheng et al. Collaborative Knowledge Base Embedding for Recommender Systems. KDD, 2016.

Hongwei et al. DKN: Deep Knowledge-Aware Network for News Recommendation. WWW, 2018.

Hongwei et al. Ripplenet-Propagating user preferences on the knowledge graph for recommender systems. CIKM, 2018.

Hongwei et al. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. KDD, 2019.

Hongwei et al. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. WWW, 2019.

Xiang et al. Reinforced Negative Sampling over Knowledge Graph for Recommendation. WWW, 2020.

Wang et al. CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems. SIGIR, 2020.

Github（2020-2021）优先级最高

链接

Knowledge-aware Recommendations

CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems. SIGIR 2020

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View. SIGIR 2020

MVIN: Learning multiview items for recommendation. SIGIR 2020

Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation. SIGIR 2020

Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach. SIGIR 2020

Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs. SIGIR 2020

SimClusters Community-Based Representations for Heterogenous Recommendations at Twitter. KDD 2020

Multi-modal Knowledge Graphs for Recommender Systems. CIKM 2020

DisenHAN Disentangled Heterogeneous Graph Attention Network for Recommendation. CIKM 2020

Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network. CIKM 2020

TGCN Tag Graph Convolutional Network for Tag-Aware Recommendation. CIKM 2020

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball. AAAI 2021

Graph Heterogeneous Multi-Relational Recommendation. AAAI 2021

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. AAAI 2021

Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph. WSDM2021

Decomposed Collaborative Filtering Modeling Explicit and Implicit Factors For Recommender Systems. WSDM 2021

Temporal Meta-path Guided Explainable Recommendation. WSDM 2021

Learning Intents behind Interactions with Knowledge Graph for Recommendation . WWW 2021

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https://github.com/hongleizhang/RSPapers（2018-2020）

Knowledge Graph for RS

Github（2020-2021）优先级最高

推荐系统和知识图谱论文推荐相关推荐

最新文章

热门文章

推荐系统和知识图谱论文推荐

KDD2020

Graph-based Recommendation（基于图，不是知识图谱）

CIKM2020

Knowledge Graph-enhanced Recommendation

WWW2021知识图谱推荐系统相关的论文

知识图谱

推荐系统

SIGIR2021

KDD2021

Research Track Papers

Applied Data Science Track Papers

https://github.com/hongleizhang/RSPapers（2018-2020）

Knowledge Graph for RS

Github（2020-2021） 优先级最高

推荐系统和知识图谱论文推荐相关推荐

最新文章

热门文章

Github（2020-2021）优先级最高