推荐系统总结

Kun et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction. arXiv, 2017.
Zhibo et al. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. KDD, 2018.
Guorui et al. Deep Interest Evolution Network for Click-Through Rate Prediction. AAAI, 2019.
Guorui et al. Deep Interest Network for Click-Through Rate Prediction. KDD, 2018.
Xiao et al. Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate. SIGIR, 2018.
Han et al. Learning Tree-based Deep Model for Recommender Systems. KDD, 2018.
Lin et al. Visualizing and Understanding Deep Neural Networks in CTR Prediction. SIGIR, 2018.
Qiwei et al. Behavior Sequence Transformer for E-commerce Recommendation in Alibaba. KDD,2019.
Wentao et al. Click-Through Rate Prediction with the User Memory Network. KDD, 2019
Yufei et al. Deep Session Interest Network for Click-Through Rate Prediction. arXiv, 2019.
Wentao et al. Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction. KDD, 2019.
Han et al. Joint Optimization of Tree-based Index and Deep Model for Recommender Systems. NIPS, 2019.
Chao et al. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. CIKM, 2019.
Qi et al. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction. KDD, 2019.
Wentao et al. Representation Learning-Assisted Click-Through Rate Prediction. arXiv, 2019.
Fuyu et al. SDM: Sequential Deep Matching Model for Online Large-scale Recommender System. CIKM, 2019.
Wentao et al. MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction. CIKM, 2020.
Zhe et al. COLD: Towards the Next Generation of Pre-Ranking System. KDD, 2020.
Weinan et al. Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction. SIGIR, 2020.
Ze et al. Deep Match to Rank Model for Personalized Click-Through Rate Prediction. AAAI, 2020.
Shu et al. Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution. AAAI, 2020.
Changhua et al. Personalized Re-ranking for Recommendation. RecSys, 2019.
Liyi et al. A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction. CIKM, 2020.
Yu et al. EdgeRec: Recommender System on Edge in Mobile Taobao. CIKM, 2020.
Yufei et al. MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction. CIKM, 2020.

[Baidu]

Xiangyu et al. Whole-Chain Recommendations. CIKM, 2020.

[Criteo]

Yuchin et al. Field-aware Factorization Machines for CTR Prediction. RecSys, 2016.

[Facebook]

Xinran et al. Practical Lessons from Predicting Clicks on Ads at Facebook. KDD, 2014.
Maxim et al. Deep Learning Recommendation Model for Personalization and Recommendation Systems. arXiv, 2019.

　　代码：https://github.com/facebookresearch/dlrm

[Google]

James et al. The YouTube Video Recommendation System. RecSys, 2010.
Jason et al. Label Partitioning For Sublinear Ranking. JMLR, 2013.
Paul et al. Deep Neural Networks for YouTube Recommendations.** RecSys, 2016.
Heng et al. Wide & Deep Learning for Recommender Systems. DLRS, 2016.
Ruoxi et al. Deep & Cross Network for Ad Click Predictions. KDD, 2017.
Alex et al. Latent Cross: Making Use of Context in Recurrent Recommender Systems. WSDM, 2018.
Alex et al. Fairness in Recommendation Ranking through Pairwise Comparisons. KDD, 2019.
Xinyang et al. Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations. RecSys, 2019.

[Huawei]

Huifeng et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. IJCAI, 2017.
Bin et al. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction. WWW, 2019.
Huifeng et al. PAL: A Position-bias Aware Learning Framework for CTR Prediction in Live Recommender Systems. RecSys, 2019.
Kai et al. Automatic Feature Engineering From Very High Dimensional Event Logs Using Deep Neural Networks. KDD, 2019.
Yishi et al. GraphSAIL Graph Structure Aware Incremental Learning for Recommender Systems. CIKM, 2020.

[JingDong]

Huifeng et al. DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction. arXiv, 2018.
Meizi et al. Micro Behaviors: A New Perspective in E-commerce Recommender Systems. WSDM, 2018.
Xiangyu et al. Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning. KDD, 2018.
Xiangyu et al. Deep Reinforcement Learning for List-wise Recommendations. arXiv, 2019.
Wenqi et al. Deep Social Collaborative Filtering. RecSys, 2019.
Wenqi et al. Graph Neural Networks for Social Recommendation. WWW, 2019.

[Meituan]

Hongwei et al. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. KDD, 2019.

[Microsoft]

（DSSM）Po-Sen et al. Learning Deep Structured Semantic Models for Web Search using Clickthrough Data. CIKM, 2013.
Ali Elkahky et al. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems. WWW, 2015.
Oren et al. ITEM2VEC: NEURAL ITEM EMBEDDING FOR COLLABORATIVE FILTERING. ICML, 2016.
Hongwei et al. DKN: Deep Knowledge-Aware Network for News Recommendation. WWW, 2018.
Guanjie et al. DRN: A Deep Reinforcement Learning Framework for News Recommendation. WWW, 2018.
John et al. Modeling and Simultaneously Removing Bias via Adversarial Neural Networks. arXiv, 2018.
Chen et al. Privileged Features Distillation at Taobao Recommendations. KDD, 2020.
Hongwei et al. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. CIKM, 2018.
Jianxun et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. KDD, 2018.
Zhongxia et al. Co-Attentive Multi-Task Learning for Explainable Recommendation. IJCAI, 2019.
Chuhan et al. Neural News Recommendation with Attentive Multi-View Learning. IJCAI, 2019.
Le et al. Personalized Multimedia Item and Key Frame Recommendation. IJCAI, 2019.
Fuyu et al. SDM: Sequential Deep Matching Model for Online Large-scale Recommender System. CIKM, 2019.
Shu et al. Session-Based Recommendation with Graph Neural Networks. AAAI, 2019.
Le et al. SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation. arXiv, 2019.

[Netflix]

Balazs et al. Session-based recommendations with recurrent neural networks. ICLR, 2016.

[Pinterest]

Ying et al. PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD, 2018
Pal et al. PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest. KDD, 2020
Yang et al. MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks. KDD, 2020

[Sina]

Junlin et al. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine. arXiv, 2019.

[Tencent]

Qitian et al. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. WWW, 2019.
Wen et al. Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction. WWW, 2020.
Ruobing et al. Deep Feedback Network for Recommendation. IJCAI, 2020
Tongwen et al. GateNet:Gating-Enhanced Deep Network for Click-Through Rate Prediction. arXiv, 2020.

[Yahoo]

Junwei et al. Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising. WWW, 2018.
Shaunak et al. Learning to Create Better Ads Generation and Ranking Approaches for Ad Creative Refinement. CIKM, 2020.

四、SOTA推荐算法

五、综述、理论介绍

[DL Recsys Intro] Deep Learning based Recommender System- A Survey and New Perspectives (UNSW 2018).pdf

[Recsys Intro] Recommender Systems Handbook (FRicci 2011)

[Recsys Intro slides] Recommender Systems An introduction (DJannach 2014)

posted @ 2021-05-31 10:14 DreamEngineer 阅读(724) 评论(0) 收藏举报

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推荐系统总结

一、传统推荐模型

二、深度学习推荐模型

三、工业级推荐系统

四、SOTA推荐算法

五、综述、理论介绍