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包含内容理解、匹配、召回、排序、 多任务、重排序等多个任务的完整推荐搜索算法库
- 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)
PaddleRec使用Apache License 2.0开源协议