Rec-Models
更多细节参考项目:https://github.com/JackHCC/Rec-Models
https://github.com/JackHCC/Rec-Models
Summary of recommendation, advertising and search models.
Recall
Papers
Paper |
Resource |
Others |
[2019阿里SDM模型] SDM: Sequential Deep Matching Model for Online Large-scale Recommender System |
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Code |
[2019阿里JTM] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems |
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Code |
[2019百度MOBIUS] MOBIUS:Towards the Next Generation of Qery Ad Matching in Baidu’s Sponsored Search |
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Code |
[2019YouTube双塔] sampling bias corrected neural modeling for large corpus item recommendations |
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Code |
[2018阿里TDM] Learning Tree-based Deep Model for Recommender Systems |
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Code |
[2018Facebook] Collaborative Multi-modal deep learning for the personalized product retrieval in Facebook Marketplace |
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Code |
[2013 DSSM模型] Learning deep structured semantic models for web search using clickthrough data |
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Code |
[2008 SVD] Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model |
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Code |
[2008] Collaborative Filtering for Implicit Feedback Datasets |
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Code |
Ranking(CTR|CVR)
Papers
Model |
Paper |
Resource |
Others |
Convolutional Click Prediction Model |
[CIKM 2015]A Convolutional Click Prediction Model |
CCPM-基于卷积的点击预测模型 |
Code |
Factorization-supported Neural Network |
[ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction |
|
Code |
Product-based Neural Network |
[ICDM 2016]Product-based neural networks for user response prediction |
PNN论文笔记 |
Code |
Wide & Deep |
[DLRS 2016]Wide & Deep Learning for Recommender Systems |
Wide&Deep模型 |
Code |
DeepFM |
[IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction |
深度推荐模型之DeepFM |
Code |
Piece-wise Linear Model |
[arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction |
MLR算法模型 |
Code |
Deep & Cross Network |
[ADKDD 2017]Deep & Cross Network for Ad Click Predictions |
谷歌经典 Deep&Cross Network原理 |
Code |
Attentional Factorization Machine |
[IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks |
推荐算法精排模型AFM:Attentional Factorization Machines |
Code |
Neural Factorization Machine |
[SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics |
NFM 模型 (论文精读)–广告&推荐 |
Code |
xDeepFM |
[KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems |
xDeepFM 原理通俗解释及代码实战 |
Code |
Deep Interest Network |
[KDD 2018]Deep Interest Network for Click-Through Rate Prediction |
阿里巴巴DIN模型详解 |
Code |
Deep Interest Evolution Network |
[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction |
DIEN算法学习笔记 |
Code |
AutoInt |
[CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks |
AutoInt:基于Multi-Head Self-Attention构造高阶特征 |
Code |
ONN |
[arxiv 2019]Operation-aware Neural Networks for User Response Prediction |
ONN: paper+code reading |
Code |
FiBiNET |
[RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction |
FiBiNET: paper reading + 实践调优经验 |
Code |
IFM |
[IJCAI 2019]An Input-aware Factorization Machine for Sparse Prediction |
IFM: 输入感知的FM模型 |
Code |
DCN V2 |
[arxiv 2020]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems |
DCNMix原理与实践 |
Code |
DIFM |
[IJCAI 2020]A Dual Input-aware Factorization Machine for CTR Prediction |
DIFM: 双重IFM模型 |
Code |
AFN |
[AAAI 2020]Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions |
|
Code |
SharedBottom |
[arxiv 2017]An Overview of Multi-Task Learning in Deep Neural Networks |
Shared-Bottom网络结构 |
Code |
ESMM |
[SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate |
ESMM详解 |
Code |
MMOE |
[KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts |
多任务学习之MMOE模型 |
Code |
PLE |
[RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations |
腾讯PCG RecSys2020最佳长论文——视频推荐场景下多任务PLE模型详解 |
Code |
Datasets
- Criteo
- Avazu
- Movielens
- Amazon
- Alibaba Click and Conversion Prediction
- UCI Machine Learning Repository
Reranking
Papers
Paper |
Resource |
Others |
[IJCAJ2018, Alibaba]. Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search |
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Code |
[SIGIR2018, Qingyao Ai]. Learning a Deep Listwise Context Model for Ranking Refinement |
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Code |
[RecSys2019, Alibaba]. Personalized Re-ranking for Recommendation |
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Code |
[CIKM2020, Alibaba]. EdgeRec-Recommender System on Edge in Mobile Taobao |
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Code |
[Artix2021, Alibaba]. Revisit Recommender System in the Permutation Prospective |
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Code |
Blogs
Calibration
Papers
Paper |
Resource |
Others |
(KDD2020, Alibaba). Calibrating User Response Predictions in Online Advertising |
|
Code |
(WWW2020, Tencent). A Simple and Empirically Strong Method for Reliable Probabilistic Predictions |
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Code |
(WWW2022, Alibaba). MBCT Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration |
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Code |
Blogs
Bid
Papers
Paper |
Resource |
Others |
[IJCAI2017, Alibaba]. Optimized Cost per Click in Taobao Display Advertising |
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Code |
[KDD2019, Alibaba]. Bid Optimization by Multivariable Control in Display Advertising |
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Code |
[AAMAS2020, ByteDance]. Optimized Cost per Mille in Feeds Advertising |
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Code |
[KDD2021, Alibaba]. A Unified Solution to Constrained Bidding in Online Display Advertising |
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Code |
[KDD2014]. Optimal Real-Time Bidding for Display Advertising |
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Code |
[KDD2015]. Bid Landscape Forecasting in Online Ad Exchange Marketplace |
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Code |
[KDD2015]. Predicting Winning Price in Real Time Bidding with Censored Data |
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Code |
[KDD2016]. User Response Learning for Directly Optimizing Campaign Performance in Display Advertising |
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Code |
[KDD2016]. Functional Bid Landscape Forecasting for Display Advertising |
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Code |
[KDD2017]. A Gamma-Based Regression for Winning Price Estimation in Real-Time Bidding Advertising |
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Code |
[KDD2018]. Bidding Machine Learning to Bid for Directly Optimizing Profits in Display Advertising |
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Code |
[KDD2019]. Deep Landscape Forecasting for Real-time Bidding Advertising |
|
Code |
Blogs
Open Resource
- DeepCTR-Torch
- EasyRec
- AI-RecommenderSystem
- DeepMatch
- PaddleRec