Skip to content

Latest commit

 

History

History
187 lines (147 loc) · 10.4 KB

KDD2021.md

File metadata and controls

187 lines (147 loc) · 10.4 KB

推荐系统工业界顶会论文总结——KDD 2021

知乎专栏

  1. Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning
    Author(Institute): Jianxun Lian(Microsoft二作)
    KeyWords: news recommender; knowledge graph; recommendation reasoning
    Dataset: MIND; Bing News

  2. Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
    Author(Institute): Jinfeng Yi(JD三作)
    KeyWords: Recommendation; Popularity Bias; Causal Reasoning
    Dataset: ML10M; Adressa; Globo; Gowalla; Yelp

  3. Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
    Author(Institute): Dongbo Xi(Meituan)
    KeyWords: Sequential Dependence; Multi-step Conversions; Multi-task Learning; Targeted Display Advertising
    Dataset: Meituan; Co-Branded Credit Cards; Ali-CCP

  4. Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
    Author(Institute): Yudan Liu(WeChat三作)
    KeyWords: Look-alike; Audience Expansion; Meta Learning; Campaign
    Dataset: Tencent; WeChat

  5. Adversarial Feature Translation for Multi-domain Recommendation
    Author(Institute): Xiaobo Hao(WeChat)
    KeyWords: recommender system; multi-domain recommendation; GAN
    Dataset: Netflix; MDR-5B

  6. Debiasing Learning based Cross-domain Recommendation
    Author(Institute): Yuxiao Dong(Alibaba二作)
    KeyWords: Debias

  7. MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems
    Author(Institute): Yuxiao Dong(Facebook)
    KeyWords: Collaborative Filtering; Recommender Systems; Graph Neural Networks; Negative Samplin
    Dataset: Alibaba; Yelp2018; Amazon

  8. Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation
    Author(Institute): Hao Gu(Tencent三作)
    KeyWords: Cold-start; Auto-Encoders; Denoise
    Dataset: WeChat

  9. Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning
    Author(Institute): Jianxun Lian(Microsoft二作)
    KeyWords: news recommender; knowledge graph; recommendation reasoning
    Dataset: MIND; Bing News

  10. A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
    Author(Institute): Léa Briand(Deezer)
    KeyWords: Recommender Systems; User Cold Start; Music Streaming Service; Semi-Personalization; Heterogeneous Data; A/B Testing
    Dataset: Deezer

  11. Architecture and Operation Adaptive Network for Online Recommendations
    Author(Institute): Lang Lang(Didi Chuxing) KeyWords: Online Recommendations

  12. SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations
    Author(Institute): Chenyi Lei(Alibaba)
    KeyWords: E-Commerce Micro-Video Recommendation; Information Transfer
    Dataset: Taobao

  13. Curriculum Meta-Learning for Next POI Recommendation
    Author(Institute): Miao Fan(Baidu三作)
    KeyWords: POI
    Dataset: Baidu Map

  14. Learning to Embed Categorical Features without Embedding Tables for Recommendation
    Author(Institute): Wang-Cheng Kang(Google)
    KeyWords: Embed Categorical Features
    Dataset: Movielens20M; Amazon Book

  15. Preference Amplification in Recommender Systems
    Author(Institute): Smriti Bhagat(Facebook二作)
    KeyWords: Recommender systems; echo chambers; filter bubbles; fixed point
    Dataset: MovieLens 10M; Yahoo

  16. Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data
    Author(Institute): Yaliang Li(Alibaba三作)
    KeyWords: Attack

  17. Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
    Author(Institute): Chunyan Miao(Alibaba三作)
    KeyWords: network initialization; recommender systems; manifold learning
    Dataset: ML-1M; Steam; Anime

  18. We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
    Author(Institute): Junqi Jin(Alibaba二作)
    KeyWords: E-commerce; Display Advertisement; Advertising Strategy Recommendation
    Dataset: online

  19. A Unified Solution to Constrained Bidding in Online Display Advertising
    Author(Institute): Yue He(Alibaba)
    KeyWords: advertising
    Dataset: Taobao

  20. Clustering for Private Interest-based Advertising
    Author(Institute): Alessandro Epasto(Google)
    KeyWords: Interest-based advertising; clustering; anonymity; privacy
    Dataset: Million Song; MovieLens

  21. Diversity driven Query Rewriting in Search Advertising
    Author(Institute): Nikit Begwani(Microsoft二作)
    KeyWords: sponsored search; query rewriting; natural language generation

  22. Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
    Author(Institute): Chao Du(Alibaba)
    KeyWords: click-through rate (CTR); exploration-exploitation trade-off; advertising system; Gaussian process
    Dataset: Amazon

  23. Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
    Author(Institute): Xiangyu Liu(Alibaba)
    KeyWords: Learning-based Mechanism Design; Neural Auction; E-commerce Advertising
    Dataset: Taobao

  24. Reinforcing Pretrained Models for Generating Attractive Text Advertisements
    Author(Institute): Xiting Wang(Microsoft)
    KeyWords: Advertisement Generation; Pretrained Language Models; Reinforcement Learning; Natural Language Generation
    Dataset: Microsoft Bing

  25. Efficient Collaborative Filtering via Data Augmentation and Step-size Optimization
    Author(Institute): Xuejun Liao(SAS Institute Inc)
    KeyWords: Collaborative Filtering; Data Augmentation
    Dataset: MovieLens 1M

  26. Efficient Data-specific Model Search for Collaborative Filtering
    Author(Institute): Quanming Yao(4Paradigm)
    KeyWords: Collaborative Filtering
    Dataset: MovieLens-100K; MovieLens-1M; Yelp; Amazon-Book

  27. ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data
    Author(Institute): Ryan A. [ Rossi(Adobe二作)
    KeyWords: Visualization recommendation; learning-based visualization recommendation; data visualization; machine learning; deep learning
    Dataset: Plot.ly

  28. PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network
    Author(Institute): Jianpeng Xu(Walmart)
    KeyWords: Recommender systems; Positive-unlabeled learning
    Dataset: Movielens; Yelp

  29. Table2Charts: Recommending Charts by Learning Shared Table Representations
    Author(Institute): Mengyu Zhou(Microsoft)
    KeyWords: Table2seq; chart recommendation; deep Q-learning; copying mechanism; search sampling; transfer learning; table representations
    Dataset: Movielens; Yelp

  30. Automated Loss Function Search in Recommendations
    Author(Institute): Chong Wang(Bytedance四作)
    KeyWords: AutoML; Recommender Systems; Loss Functions
    Dataset: Criteo; ML-20m

  31. Bootstrapping Recommendations at Chrome Web Store
    Author(Institute): Zhen Qin(Google)
    KeyWords: learning to rank; generalized additive models; text embedding
    Dataset: CWS

  32. Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
    Author(Institute): Chang Zhou(Alibaba)
    KeyWords: candidate generation; bias reduction; inverse propensity weighting; contrastive learning; negative sampling
    Dataset: ML-1M; Beauty; Steam

  33. Device-Cloud Collaborative Learning for Recommendation
    Author(Institute): Jiangchao Yao(Alibaba)
    KeyWords: On-device Intelligence; Cloud Computing
    Dataset: Amazon; Movielens-1M; Taobao

  34. FleetRec: Large-Scale Recommendation Inference on Hybrid GPU-FPGA Clusters
    Author(Institute): Kai Zeng(Alibaba四作)
    KeyWords: scalable recommendation

  35. Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters
    Author(Institute): Jiangchao Yao(Facebook)
    KeyWords: CPU Clusters

  36. Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
    Author(Institute): Zhuoxuan Jiang(Tencent二作); Dong-Dong Chen(JD三作); Dongsheng Li (Microsoft五作)
    KeyWords: graph representation learning; multi-task learning; live streaming E-Commence; product recommendation
    Dataset: LSEC-Small; LSECLarge

  37. Sliding Spectrum Decomposition for Diversified Recommendation
    Author(Institute): Yanhua Huang(Xiaohongshu)
    KeyWords: Diversified Recommendation; Sliding Spectrum Decomposition; Item Embedding; Determinantal Point Process; CB2CF

  38. Towards the D-Optimal Online Experiment Design for Recommender Selection
    Author(Institute): Da Xu(Walmart)
    KeyWords: Recommender Selection
    Dataset: Walmart