| 内容理解 | TextCnn(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [EMNLP 2014]Convolutional neural networks for sentence classication |
| 内容理解 | TagSpace(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [EMNLP 2014]TagSpace: Semantic Embeddings from Hashtags |
| 匹配 | DSSM(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [CIKM 2013]Learning Deep Structured Semantic Models for Web Search using Clickthrough Data |
| 匹配 | Match-Pyramid(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [AAAI 2016]Text Matching as Image Recognition |
| 匹配 | MultiView-Simnet(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [WWW 2015]A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems |
| 匹配 | KIM(文档) | - | x | x | >=2.1.0 | [SIGIR 2021]Personalized News Recommendation with Knowledge-aware Interactive Matching |
| 召回 | TDM | - | ✓ | >=1.8.0 | 1.8.5 | [KDD 2018]Learning Tree-based Deep Model for Recommender Systems |
| 召回 | FastText | - | x | x | 1.8.5 | [EACL 2017]Bag of Tricks for Efficient Text Classification |
| 召回 | MIND(文档) | Python CPU/GPU | x | x | >=2.1.0 | [2019]Multi-Interest Network with Dynamic Routing for Recommendation at Tmall |
| 召回 | Word2Vec(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [NIPS 2013]Distributed Representations of Words and Phrases and their Compositionality |
| 召回 | DeepWalk(文档) | Python CPU/GPU | x | x | >=2.1.0 | [SIGKDD 2014]DeepWalk: Online Learning of Social Representations |
| 召回 | SSR | - | ✓ | ✓ | 1.8.5 | [SIGIR 2016]Multtti-Rate Deep Learning for Temporal Recommendation |
| 召回 | Gru4Rec(文档) | - | ✓ | ✓ | 1.8.5 | [2015]Session-based Recommendations with Recurrent Neural Networks |
| 召回 | Youtube_dnn | - | ✓ | ✓ | 1.8.5 | [RecSys 2016]Deep Neural Networks for YouTube Recommendations |
| 召回 | NCF(文档) | Python CPU/GPU | ✓ | ✓ | >=2.1.0 | [WWW 2017]Neural Collaborative Filtering |
| 召回 | TiSAS | - | ✓ | ✓ | >=2.1.0 | [WSDM 2020]Time Interval Aware Self-Attention for Sequential Recommendation |
| 召回 | ENSFM | - | ✓ | ✓ | >=2.1.0 | [IW3C2 2020]Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation |
| 召回 | MHCN | - | ✓ | ✓ | >=2.1.0 | [WWW 2021]Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation |
| 召回 | GNN | - | ✓ | ✓ | 1.8.5 | [AAAI 2019]Session-based Recommendation with Graph Neural Networks |
| 召回 | RALM | - | ✓ | ✓ | 1.8.5 | [KDD 2019]Real-time Attention Based Look-alike Model for Recommender System |
| 排序 | Logistic Regression(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | / |
| 排序 | Dnn(文档) | Python CPU/GPU | ✓ | ✓ | >=2.1.0 | / |
| 排序 | FM(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [IEEE Data Mining 2010]Factorization machines |
| 排序 | BERT4REC | - | ✓ | x | >=2.1.0 | [CIKM 2019]BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer |
| 排序 | FAT_DeepFFM | - | ✓ | x | >=2.1.0 | [2019]FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine |
| 排序 | FFM(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [RECSYS 2016]Field-aware Factorization Machines for CTR Prediction |
| 排序 | FNN | - | ✓ | x | 1.8.5 | [ECIR 2016]Deep Learning over Multi-field Categorical Data |
| 排序 | Deep Crossing | - | ✓ | x | 1.8.5 | [ACM 2016]Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features |
| 排序 | Pnn | - | ✓ | x | 1.8.5 | [ICDM 2016]Product-based Neural Networks for User Response Prediction |
| 排序 | DCN(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [KDD 2017]Deep & Cross Network for Ad Click Predictions |
| 排序 | NFM | - | ✓ | x | 1.8.5 | [SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics |
| 排序 | AFM | - | ✓ | x | 1.8.5 | [IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks |
| 排序 | DMR(文档) | Python CPU/GPU | x | x | >=2.1.0 | [AAAI 2020]Deep Match to Rank Model for Personalized Click-Through Rate Prediction |
| 排序 | DeepFM(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction |
| 排序 | xDeepFM(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems |
| 排序 | DIN(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [KDD 2018]Deep Interest Network for Click-Through Rate Prediction |
| 排序 | DIEN(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction |
| 排序 | GateNet(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [SIGIR 2020]GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction |
| 排序 | DLRM(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [CoRR 2019]Deep Learning Recommendation Model for Personalization and Recommendation Systems |
| 排序 | NAML(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [IJCAI 2019]Neural News Recommendation with Attentive Multi-View Learning |
| 排序 | DIFM(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [IJCAI 2020]A Dual Input-aware Factorization Machine for CTR Prediction |
| 排序 | DeepFEFM(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [arXiv 2020]Field-Embedded Factorization Machines for Click-through rate prediction |
| 排序 | BST(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [DLP_KDD 2019]Behavior Sequence Transformer for E-commerce Recommendation in Alibaba |
| 排序 | AutoInt | - | ✓ | x | >=2.1.0 | [CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks |
| 排序 | Wide&Deep(文档) | Python CPU/GPU | ✓ | x | >=2.1.0 | [DLRS 2016]Wide & Deep Learning for Recommender Systems |
| 排序 | Fibinet | - | ✓ | ✓ | 1.8.5 | [RecSys19]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction |
| 排序 | FLEN | - | ✓ | ✓ | >=2.1.0 | [2019]FLEN: Leveraging Field for Scalable CTR Prediction |
| 排序 | DeepRec | - | ✓ | ✓ | >=2.1.0 | [2017]Training Deep AutoEncoders for Collaborative Filtering |
| 排序 | AutoFIS | - | ✓ | ✓ | >=2.1.0 | [KDD 2020]AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction |
| 排序 | DCN_V2 | - | ✓ | ✓ | >=2.1.0 | [WWW 2021]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems |
| 排序 | DSIN | - | ✓ | ✓ | >=2.1.0 | [IJCAI 2019]Deep Session Interest Network for Click-Through Rate Prediction |
| 排序 | SIGN(文档) | Python CPU/GPU | ✓ | ✓ | >=2.1.0 | [AAAI 2021]Detecting Beneficial Feature Interactions for Recommender Systems |
| 排序 | IPRec(文档) | - | ✓ | ✓ | >=2.1.0 | [SIGIR 2021]Package Recommendation with Intra- and Inter-Package Attention Networks |
| 排序 | FGCNN | - | ✓ | ✓ | >=2.1.0 | [WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction |
| 排序 | DPIN(文档) | Python CPU/GPU | ✓ | ✓ | >=2.1.0 | [SIGIR 2021]Deep Position-wise Interaction Network for CTR Prediction |
| 多任务 | AITM | - | ✓ | ✓ | >=2.1.0 | [KDD 2021]Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising |
| 多任务 | PLE(文档) | Python CPU/GPU | ✓ | ✓ | >=2.1.0 | [RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations |
| 多任务 | ESMM(文档) | Python CPU/GPU | ✓ | ✓ | >=2.1.0 | [SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate |
| 多任务 | MMOE(文档) | Python CPU/GPU | ✓ | ✓ | >=2.1.0 | [KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts |
| 多任务 | ShareBottom(文档) | Python CPU/GPU | ✓ | ✓ | >=2.1.0 | [1998]Multitask learning |
| 多任务 | Maml(文档) | Python CPU/GPU | x | x | >=2.1.0 | [PMLR 2017]Model-agnostic meta-learning for fast adaptation of deep networks |
| 多任务 | DSelect_K(文档) | - | x | x | >=2.1.0 | [NeurIPS 2021]DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning |
| 多任务 | ESCM2 | - | x | x | >=2.1.0 | [SIGIR 2022]ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation |
| 多任务 | MetaHeac | - | x | x | >=2.1.0 | [KDD 2021]Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising |
| 重排序 | Listwise | - | ✓ | x | 1.8.5 | [2019]Sequential Evaluation and Generation Framework for Combinatorial Recommender System |
