推荐系统顶会论文总结——WSDM 2021

WSDM 2021

1.Real-time Relevant Recommendation Suggestion
Author(Institute): Ruobing Xie

KeyWords：relevant recommendation

Dataset：RS-331M

2.Diverse User Preference Elicitation with Multi-Armed Bandits
Author(Institute): Filip Radlinski

KeyWords：preference elicitation; diversity; bandits

Dataset：Movielens; Amazon

3.User Response Models to Improve a REINFORCE Recommender System
Author(Institute): Minmin Chen

KeyWords: Auxiliary Tasks; User Response Models; Reinforcement Learning

4.A Black-Box Attack Model for Visually-Aware Recommenders
Author(Institute): Rami Cohen

KeyWords: Attacks; Adversarial Examples

Dataset: Amazon

5.Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction
Author(Institute): Zhen Qin

KeyWords: Unbiased learning to rank; implicit feedback

Dataset: GMail

6.Unbiased Learning to Rank in Feeds Recommendation
Author(Institute): Li He; Dawei Yin

KeyWords: Feeds Recommendation; Learning to Rank; Unbiased Learning

Dataset: JD

7.Improving Cloud Storage Search with User Activity
Author(Institute): Rolf Jagerman

KeyWords: User activity logs; Learning to Rank

Dataset: Google Drive

8.Network for Sequential Recommendation
Author(Institute): Jianwei Zhang

KeyWords: Sequential recommendation; Sparse-interest network; Multi-interest extraction

Dataset: MovieLens Amazon; Taobao

9.DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
Author(Institute): Junwei Pan

KeyWords: Deep acceleration; ad serving; structural pruning; preconditioner; lightweight models; fast inference; low memory

Dataset: Criteo; Avazu

10.Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction
Author(Institute): Hao Qian

KeyWords: CTR Prediction; Interactive Attention Network

Dataset: Amazon

11.Bipartite Graph Embedding via Mutual Information Maximization
Author(Institute): Bin Wang

KeyWords: Bipartite Graph Embedding

Dataset: DBLP; ML-10; ML-1; Wikiped

12.Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
Author(Institute): Riku Togashi

KeyWords: knowledge graph; cold-start recommendation; knowledge-aware recommendation; graph neural networks; semi-supervised learning

Dataset: MovieLens1M; Last.FM; BookCrossing

13.Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Author(Institute): Zhe Chen

KeyWords: Ensemble; Neuron Activation; Prediction Uncertainty

Dataset: MovieLens; Criteo

14.Combating Selection Biases in Recommender Systems with A Few Unbiased Ratings
Author(Institute): Xiaojie Wang

KeyWords: Biases

Dataset: Music; Coat

15.Learning User Representations with Hypercuboids for Recommender Systems
Author(Institute): Huoyu Liu

KeyWords: Hypercuboids; User Representation

Dataset: Amazon-Books; Amazon-Movies&TVs; AmazonCDs; E-commerce

16.Origin-Aware Next Destination Recommendation with Personalized Preference Attention
Author(Institute): Nicholas Lim

KeyWords: Spatio-Temporal

17.Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems
Author(Institute): Xuezhi Wang

KeyWords: compositional fairness; ranking fairness

Dataset: the German Credit data

18.Enhancing Neural Recommender Models through Domain-Specific Concordance
Author(Institute): Ananth Balashankar

KeyWords: Domain-Specific Concordance

Dataset: MIMIC-III; MovieLens; Last.fm

19.Towards Long-term Fairness in Recommendation
Author(Institute): Junfeng Ge

KeyWords: Long-term Fairness; Reinforcement Learning; Constrained Policy Optimization; Unbiased Recommendation

Dataset: Movielens100K; Movielens1M

20.Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction
Author(Institute): Murat Ali Bayir

KeyWords: Random forest；Causal learning；Transfer learning；Robust prediction models；Covariate shifts

21.Explanation as a Defense of Recommendation
Author(Institute): Hongbo Deng

KeyWords: Explainable Recommendation; Natural Language Generation; Sentiment Alignment

Dataset: Yelp; Ratebeer

22.Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks
Author(Institute): Bofang Li

KeyWords: Heterogeneous Graph; E-Commerce; Sharing Recommendation

Dataset: Taobao