推荐系统顶会论文总结——SIGIR 2021

SIGIR 2021

1.Looking at CTR Prediction Again: Is Attention All You Need?
Author(Institute): Yuan Cheng

KeyWords: click-through rate prediction; neural networks; self-attention mechanism; factorization machines; discrete choice model

Dataset: Criteo; Avazu

2.Mix Cache-based Distributed Training System for CTR Models with Huge Embedding Table
Author(Institute): Huifeng Guo

KeyWords: CTR Prediction; Recommendation; Distributed Training System

Dataset: Criteo-TB

3.A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction
Author(Institute): Yumeng Li

KeyWords: Click-through Rate Prediction; Gradient-based Neural Architecture Search; Feature Interaction; Interaction Ensemble

Dataset: Criteo; Avazu; Movielens; Frappe

4.Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
Author(Institute): Yumeng Li

KeyWords: Online advertising; CTR prediction; Cold start; Deep learning

Dataset: ML-1M; Taobao; News feed

5.GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction
Author(Institute): Hongliang Fei

KeyWords: CTR

6.Deep User Match Network for Click-Through Rate Prediction
Author(Institute): Zai Huang

KeyWords: CTR

7.RLNF: Reinforcement Learning based Noise Filtering for Click-Through Rate Prediction
Author(Institute): Pu Zhao

KeyWords: CTR

Dataset: Avazu

8.Category-aware Collaborative Sequential Recommendation
Author(Institute): Chong Wang

KeyWords: Sequential recommendation

9.Learning to Ask Appropriate Questions in Conversational Recommendation
Author(Institute): Hao Wang

KeyWords: Conversational recommender systems; knowledge graph; clarifying question; preference mining

Dataset: MovieLens-1M; DBbook2014

10.Personalized News Recommendation with Knowledge-aware Interactive Matching
Author(Institute): Fangzhao Wu

KeyWords: News Recommendation; Interactive Matching; Single-Tower

Dataset: MIND; Feeds

11.Empowering News Recommendation with Pre-trained Language Models
Author(Institute): Fangzhao Wu

KeyWords: News Recommendation; pre-trained language model

Dataset: MIND; Multilingual

12.Graph Meta Network for Multi-Behavior Recommendation
Author(Institute): Chao Huang

KeyWords: Multi-Behavior Recommendation

Dataset: Taobao-Data; Beibei-Data; IJCAI-Contest

13.AutoDebias: Learning to Debias for Recommendation
Author(Institute): Guli Lin

KeyWords: Recommendation; Bias; Debias; Meta-learning

Dataset: Yahoo!R3; Coat; Simulation

14.Causal Intervention for Leveraging Popularity Bias in Recommendation
Author(Institute): Chonggang Song

KeyWords: Popularity Bias; Causal Intervention

Dataset: Kwai; Douban Movie; Tencent

13.Underestimation Refinement: A General Enhancement Strategy for Exploration in Recommendation Systems
Author(Institute): Yuhai Song

KeyWords: Contextual Bandit

Dataset: Yahoo

14.StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
Author(Institute): Fajie Yuan

KeyWords: Knowledge Transfer; Training acceleration

Dataset: ML20; Kuaibao; ColdRec

15.Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback
Author(Institute): Hanjing Su

KeyWords: Streaming Recommendation

Dataset: WeChat

16.Package Recommendation with Intra- and Inter-Package Attention Networks
Author(Institute): Chen Li

KeyWords: Package Recommendation; Attention

17.Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation
Author(Institute): Zheng Liu

KeyWords: Low-Rank Self-Attention; Next-Item Recommendation

Dataset: Yelp; Books; ML-1M

18.Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
Author(Institute): Yaliang Li

KeyWords: Conversational Recommendation; Reinforcement Learning; Graph Representation Learning

Dataset: LastFM; Yelp; Taobao

19.Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation
Author(Institute): Fangzhao Wu

KeyWords: Recurrent Graph Convolution; Knowledge Pruning; News Recommendation

Dataset: MIND; Adressa

20.AMM: Attentive Multi-field Matching for News Recommendation
Author(Institute): Qi Zhang

KeyWords: News Recommendation

21.RMBERT: News Recommendation via Recurrent Reasoning Memory Network over BERT
Author(Institute): Qinglin Jia

KeyWords: news recommendation; BERT

22.FedCT: Federated Collaborative Transfer for Recommendation
Author(Institute): Wenhui Yu

KeyWords: Federated Collaborative Transfer

23.Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
Author(Institute): Yongchun Zhu

KeyWords: Cross-domain Recommendation; Meta Learning; Cold-start

Dataset: Amazon; Douban

24.Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems
Author(Institute): Huiyuan Chen(Visa)

KeyWords: Structured Graph Convolutional Networks

25.Self-supervised Graph Learning for Recommendation
Author(Institute): Jianxun Lian

KeyWords: Collaborative filtering; Graph Neural Network; Self-supervised Learning; Long-tail Recommendation

Dataset: Yelp2018; Amazon-Book; Alibaba-iFashion

26.Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement
Author(Institute): Fajie Yuan

KeyWords: Soft Target Enhancement

27.PreSizE: Predicting Size in E-Commerce using Transformers
Author(Institute): Yotam Eshel

KeyWords: Size Prediction; Transformers; Deep-Learning

Dataset: eBay

28.Did you buy it already? Detecting Users Purchase-State From Their Product-Related Questions
Author(Institute): Lital Kuchy

KeyWords: Purchase state classification; Product question answering

Dataset: Amazon

29.Path-based Deep Network for Candidate Item Matching in Recommenders
Author(Institute): Houyi Li

KeyWords: Deep Learning; Recommendation Systems

Dataset: MovieLens; Pinterest; Amazon Books

30.How Powerful are Interest Diffusion on Purchasing Prediction: A Case Study of Taocode
Author(Institute): Shen Fa

KeyWords: purchasing prediction; information diffusion; GNN; Taocode

Dataset: Taocode

31.Enhanced Doubly Robust Learning for Debiasing Post-Click Conversion Rate Estimation
Author(Institute): Lixin Zou

KeyWords: Selection Bias; Missing-Not-At-Random Data; Doubly Robust; Postclick Conversion Rate Estimation

Dataset: MovieLens

32.On Interpretation and Measurement of Soft Attributes for Recommendation
Author(Institute): Filip Radlinski

KeyWords: Soft attributes; Recommendation critiquing; Preference feedback

Dataset: MovieLens

33.FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation

Author(Institute): Yanrong Kang

KeyWords: Meta-learning

33.Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Author(Institute): Ruobing Xie

KeyWords: Cold-start Recommendation; Item ID Embedding; Warm Up; Meta Network

Dataset: MovieLens-1M; Taobao Display Ad Click; CIKM2019 EComm AI

34.Fairness among New Items in Cold Start Recommender Systems
Author(Institute): Jingu Kim

KeyWords: fairness; cold start recommendation

Dataset: ML1M; ML20M; CiteULike; XING

35.Long-Tail Hashing
Author(Institute): Yuqing Hou

KeyWords: learning to hash; long-tail datasets; memory network; large-scale multimedia retrieval

Dataset: Cifar100; ImageNet100

36.Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization
Author(Institute): Ramin Raziperchikolaei

KeyWords: hybrid recommender systems; neural networks; regularization

Dataset: ml100k; ml1m; Ichiba

37.Cross-Batch Negative Sampling for Training Two-Tower Recommenders
Author(Institute): Jieming Zhu

KeyWords: Recommender systems; information retrieval; neural networks

Dataset: Amazon

38.Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction
Author(Institute): Feng Li

KeyWords: CTR prediction; Pre-trained GNNs; Cross Features; Explicit Fashion

Dataset: MovieLens