推荐系统顶会论文总结——KDD 2021

KDD2021

1.Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning
Author(Institute): Jianxun Lian

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

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

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

KeyWords: Look-alike; Audience Expansion; Meta Learning; Campaign

Dataset: Tencent; WeChat

5.Adversarial Feature Translation for Multi-domain Recommendation
Author(Institute): Xiaobo Hao

KeyWords: recommender system; multi-domain recommendation; GAN

Dataset: Netflix; MDR-5B

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

KeyWords: Debias

7.MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems
Author(Institute): Yuxiao Dong

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

KeyWords: Cold-start; Auto-Encoders; Denoise

Dataset: WeChat

9.Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning
Author(Institute): Jianxun Lian

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

KeyWords: Online Recommendations

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

KeyWords: E-Commerce Micro-Video Recommendation; Information Transfer

Dataset: Taobao

13.Curriculum Meta-Learning for Next POI Recommendation
Author(Institute): Miao Fan

KeyWords: POI

Dataset: Baidu Map

14.Learning to Embed Categorical Features without Embedding Tables for Recommendation
Author(Institute): Wang-Cheng Kang

KeyWords: Embed Categorical Features

Dataset: Movielens20M; Amazon Book

15.Preference Amplification in Recommender Systems
Author(Institute): Smriti Bhagat

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

KeyWords: Attack

17.Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
Author(Institute): Chunyan Miao

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

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

KeyWords: advertising

Dataset: Taobao

20.Clustering for Private Interest-based Advertising
Author(Institute): Alessandro Epasto

KeyWords: Interest-based advertising; clustering; anonymity; privacy

Dataset: Million Song; MovieLens

21.Diversity driven Query Rewriting in Search Advertising
Author(Institute): Nikit Begwani

KeyWords: sponsored search; query rewriting; natural language generation

22.Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
Author(Institute): Chao Du

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

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

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

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

KeyWords: Visualization recommendation; learning-based visualization recommendation; data visualization; machine learning; deep learning

Dataset: http://Plot.ly

28.PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network
Author(Institute): Jianpeng Xu

KeyWords: Recommender systems; Positive-unlabeled learning

Dataset: Movielens; Yelp

29.Table2Charts: Recommending Charts by Learning Shared Table Representations
Author(Institute): Mengyu Zhou

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

KeyWords: AutoML; Recommender Systems; Loss Functions

Dataset: Criteo; ML-20m

31.Bootstrapping Recommendations at Chrome Web Store
Author(Institute): Zhen Qin

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

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

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

KeyWords: scalable recommendation

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

KeyWords: CPU Clusters

36.Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Author(Institute): Zhuoxuan Jiang; Dong-Dong Chen; Dongsheng Li

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

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

KeyWords: Recommender Selection

Dataset: Walmart