PaddleRec
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项目背景
1. 推荐系统背景知识
2. 分布式深度学习介绍
入门教程
1. PaddleRec 功能介绍
2. 动态图模式介绍
3. 静态图模式介绍
4. 分布式模式介绍
进阶教程
1. PaddleRec 贡献代码
2. 自定义Reader
3. 自定义模型
4. PaddleRec config.yaml配置说明
5. 可视化功能介绍
6. 在线Serving部署
7. Paddle Inference的使用方法
8. Benchmark
9. 推荐全流程
模型介绍
1. tagspace (TagSpace: Semantic Embeddings from Hashtags)
2. textcnn (Convolutional neural networks for sentence classication)
3. dssm (Learning Deep Structured Semantic Models for Web Search using Clickthrough Data)
4. match-pyramid (Text Matching as Image Recognition)
5. multiview-simnet (A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems)
6. kim (Personalized News Recommendation with Knowledge-aware Interactive Matching)
7. gru4rec (Session-based Recommendations with Recurrent Neural Networks)
8. deepwalk (DeepWalk: Online Learning of Social Representations)
9. mind (Multi-Interest Network with Dynamic Routing for Recommendation at Tmall)
10. ncf (Neural Collaborative Filtering)
11. word2vec (Distributed Representations of Words and Phrases and their Compositionality)
12. ENSFM (Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation)
13. TiSASRec-paddle (Time Interval Aware Self-Attention for Sequential Recommendation)
14. bst (Behavior Sequence Transformer for E-commerce Recommendation in Alibaba)
15. dcn (Deep & Cross Network for Ad Click Predictions)
16. deepfefm (Field-Embedded Factorization Machines for Click-through rate prediction)
17. deepfm (DeepFM: A Factorization-Machine based Neural Network for CTR Prediction)
18. dien (Deep Interest Evolution Network for Click-Through Rate Prediction)
19. difm (A Dual Input-aware Factorization Machine for CTR Prediction)
20. din (Deep Interest Network for Click-Through Rate Prediction)
21. dlrm (Deep Learning Recommendation Model for Personalization and Recommendation Systems)
22. dmr (Deep Match to Rank Model for Personalized Click-Through Rate Prediction)
23. dnn ( - )
24. fgcnn (Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction)
25. ffm (Field-aware Factorization Machines for CTR Prediction)
26. fm ( - )
27. gatenet (GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction)
28. logistic_regression ( - )
29. naml (Neural News Recommendation with Attentive Multi-View Learning)
30. wide&deep (Wide & Deep Learning for Recommender Systems)
31. xdeepfm (xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems)
32. BERT4Rec模型 (Sequential Recommendation with Bidirectional Encoder Representations from Transformer)
33. FAT_DeepFFM (FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine)
34. DeepRec (Training Deep AutoEncoders for Collaborative Filtering)
35. AutoFIS (Automatic Feature Interaction Selection in Factorization Models)
36. sign (Detecting Beneficial Feature Interactions for Recommender Systems)
37. dsin (Deep Session Interest Network for Click-Through Rate Prediction)
38. iprec (Package Recommendation with Intra- and Inter-Package Attention Networks)
39. esmm (Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate)
40. maml (Model-agnostic meta-learning for fast adaptation of deep networks)
41. mmoe (Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts)
42. ple (Progressive Layered Extraction : A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations)
43. share_bottom (Multitask learning)
44. DSelect-k(DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning)
45. metaheac (Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising)
46. escm2 (ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation)
47. aitm (Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising)
45. metaheac (Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising)
FAQ
1. 常见问题FAQ
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