推荐系统相关论文汇总
介绍
- 截至2023-12-30,本仓库收集汇总了推荐系统领域相关论文共827篇,涉及:召回,粗排,精排,重排,多任务,多场景,多模态,冷启动,校准, 纠偏,多样性,公平性,反馈延迟,蒸馏,对比学习,因果推断,Look-Alike,Learning-to-Rank,强化学习等领域,本仓库会跟踪业界进展,持续更新。
- 因文件名特殊字符的限制,故论文title中所有的
:
都改为了-
,检索时请注意。 - 文件名前缀中带有
[]
的,表明本人已经通读过,第一个[]
中为论文年份,第二个[]
中为发表机构或公司(可选),第三个[]
中为论文提出的model或method的简称(可选)。 - 在某些一级分类下面,还有若干二级分类;一篇论文可能应该涉及多个二级分类(例如用对比学习的方法做召回),最终我会将论文放在较主要的那一类下;分类也会随时调整优化,欢迎在
issue
中提出宝贵意见。 - 若您是文章作者,且不希望您的论文出现在这里,请在
issue
中提出,我核实后会马上下架。 - 关于排序算法的一些实现,请见我的另一个repo: https://github.com/tangxyw/RecAlgorithm
- 本仓库仅供交流学习使用,不做任何商业目的。
联系方式
论文目录
- Rank
- Industry
- Pre-Rank
- Re-Rank
- Match
- Multi-Task
- Multi-Modal
- Multi-Scenario
- Debias
- Calibration
- Distillation
- Feedback-Delay
- ContrastiveLearning
- Cold-Start
- Learning-to-Rank
- Fairness
- Look-Alike
- CausalInference
- Diversity
- ABTest
- ReinforcementLearning
Rank
- [2009][BPR] Bayesian Personalized Ranking from Implicit Feedback
- [2010][FM] Factorization Machines
- [2014][Facebook][GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook
- [2016][UCL][FNN] Deep Learning over Multi-field Categorical Data
- [2016][Microsft][Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features
- [2016][Google][Wide&Deep] Wide & Deep Learning for Recommender Systems
- [2016][SJTU][PNN] Product-based Neural Networks for User Response Prediction
- [2016][NTU][FFM] Field-aware Factorization Machines for CTR Prediction
- [2017][Stanford][DCN] Deep & Cross Network for Ad Click Predictions
- [2017][NUS][NFM] Neural Factorization Machines for Sparse Predictive Analytics
- [2017][ZJU][AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks
- [2017][NUS][NCF] Neural Collaborative Filtering
- [2017][Alibaba][MLR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
- [2017][Huawei][DeepFM] A Factorization-Machine based Neural Network for CTR Prediction
- [2018][USTC][xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems
- [2019][AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks
- DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
- SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS
Industry
- [2016][Youtube] Deep Neural Networks for YouTube Recommendations
- [2016][Microsoft] User Fatigue in Online News Recommendation
- [2017][Alibaba][DIN] Deep Interest Network for Click-Through Rate Prediction
- [2017][Alibaba][ATRank] ATRank - An Attention-Based User Behavior Modeling Framework for Recommendation
- [2018][Alibaba][DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction
- [2018][FwFM] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
- [2018][JD] Micro Behaviors - A New Perspective in E-commerce Recommender Systems
- [2018][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb
- [2019][Alibaba][DSIN] Deep Session Interest Network for Click-Through Rate Prediction
- [2019][Alibaba][BST] Behavior Sequence Transformer for E-commerceRecommendation in Alibaba
- [2019][Weibo][FiBiNET] FiBiNET - Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
- [2019][Alibaba][MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
- [2019][Airbnb] Applying Deep Learning To Airbnb Search
- [2020][Alibaba][CAN] CAN - Revisiting Feature Co-Action for Click-Through Rate Prediction
- [2020][Alibaba][SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
- [2020][Alibaba][DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction
- [2021][Fliggy] [DMSN] Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning
- [2021][Weibo][MaskNet] MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
- [2021][Huawei][AutoDis] An Embedding Learning Framework for Numerical Features in CTR Prediction
- [2021][Alibaba][DINMP] A Non-sequential Approach to Deep User Interest Model for Click-Through Rate Prediction
- [2021][Google] Bootstrapping Recommendations at Chrome Web Store
- [2022][Alibaba] Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
- [2022][Google] On the Factory Floor - ML Engineering for Industrial-Scale Ads Recommendation Models
- [[2023][Huawei] Ten Challenges in Industrial Recommender