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推荐系统工业界顶会论文总结——WSDM 2021

知乎专栏

  1. Real-time Relevant Recommendation Suggestion
    Author(Institute): Ruobing Xie(WeChat)
    KeyWords：relevant recommendation
    Dataset：RS-331M

  2. Diverse User Preference Elicitation with Multi-Armed Bandits
    Author(Institute): Filip Radlinski(Google二作)
    KeyWords：preference elicitation; diversity; bandits
    Dataset：Movielens; Amazon

  3. User Response Models to Improve a REINFORCE Recommender System
    Author(Institute): Minmin Chen( Google)
    KeyWords: Auxiliary Tasks; User Response Models; Reinforcement Learning

  4. A Black-Box Attack Model for Visually-Aware Recommenders
    Author(Institute): Rami Cohen(Intuit)
    KeyWords: Attacks; Adversarial Examples
    Dataset: Amazon

  5. Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction
    Author(Institute): Zhen Qin( Google)
    KeyWords: Unbiased learning to rank; implicit feedback
    Dataset: GMail

  6. Unbiased Learning to Rank in Feeds Recommendation
    Author(Institute): Li He(JD四作); Dawei Yin(Baidu五作)
    KeyWords: Feeds Recommendation; Learning to Rank; Unbiased Learning
    Dataset: JD

  7. Improving Cloud Storage Search with User Activity
    Author(Institute): Rolf Jagerman(Google)
    KeyWords: User activity logs; Learning to Rank
    Dataset: Google Drive

  8. Network for Sequential Recommendation
    Author(Institute): Jianwei Zhang (Alibaba)
    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(Yahoo)
    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(Ant Group)
    KeyWords: CTR Prediction; Interactive Attention Network
    Dataset: Amazon

  11. Bipartite Graph Embedding via Mutual Information Maximization
    Author(Institute): Bin Wang(Xiaomi三作)
    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 (CyberAgent)
    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(Google)
    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 (Amazon)
    KeyWords: Biases
    Dataset: Music; Coat

  15. Learning User Representations with Hypercuboids for Recommender Systems
    Author(Institute): Huoyu Liu (Alibaba)
    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 (GrabTaxi Holdings)
    KeyWords: Spatio-Temporal

  17. Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems
    Author(Institute): Xuezhi Wang(Google)
    KeyWords: compositional fairness; ranking fairness
    Dataset: the German Credit data

  18. Enhancing Neural Recommender Models through Domain-Specific Concordance
    Author(Institute): Ananth Balashankar (Google)
    KeyWords: Domain-Specific Concordance
    Dataset: MIMIC-III; MovieLens; Last.fm

  19. Towards Long-term Fairness in Recommendation
    Author(Institute): Junfeng Ge(Alibaba三作)
    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(Microsoft二作)
    KeyWords: Random forest；Causal learning；Transfer learning；Robust prediction models；Covariate shifts

  21. Explanation as a Defense of Recommendation
    Author(Institute): Hongbo Deng(Alibaba二作)
    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(Alibaba二作)
    KeyWords: Heterogeneous Graph; E-Commerce; Sharing Recommendation
    Dataset: Taobao