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deep_ctr's Introduction

点击预估模型

1. Recall

算法 论文 公众号或知乎文章介绍
Word2vec Efficient Estimation of Word Representations in Vector Space
YouTubeNet Deep Neural Networks for YouTube Recommendations 推荐系统召回模型之YouTubeNet
DSSM Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations 实践DSSM召回模型
MIND Multi-Interest Network with Dynamic Routing for Recommendation at Tmall 推荐系统召回模型之MIND用户多兴趣网络实践

2. Rank

算法 论文 公众号文章介绍
FFM Field-aware Factorization Machines for CTR Prediction FFM算法原理及Bi-FFM算法实现
Wide & Deep Wide & Deep Learning for Recommender Systems
NFM Neural Factorization Machines for Sparse Predictive Analytics NFM模型理论与实践
AFM Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks 注意力机制在深度推荐算法中的应用之AFM模型
DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction DeepFM实践
BST Behavior sequence transformer for e-commerce recommendation in Alibaba Transformer 在美团搜索排序中的实践

3. Multi-Task

算法 论文 公众号文章介绍
ESMM Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate ESMM多任务学习算法在推荐系统中的应用
MMoE Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts 多任务学习之MMOE模型

4. Recall_ANN

算法 开源地址 公众号文章介绍
Annoy https://github.com/spotify/annoy Annoy最近邻检索技术之 “图片检索”
Faiss https://github.com/facebookresearch/faiss

代码参考

https://github.com/shenweichen/DeepCTR

https://github.com/shenweichen/DeepMatch

待学习及分享

Recall

Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring

Controllable Multi-Interest Framework for Recommendation， 代码：https://github.com/THUDM/ComiRec

Pre-Rank

COLD: Towards the Next Generation of Pre-Ranking System

Rank

DIN：Deep Interest Network for Click-Through Rate Prediction

DIEN：Deep Interest Evolution Network for Click-Through Rate Prediction， 代码： https://github.com/mouna99/dien

MIMN：Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

Search-based Interest Model：Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction

Multi-Task

YouTube，2019: Recommending What Video to Watch Next-A Multitask Ranking System

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