Call for model implementations

[zhujiem]

• Wukong: Towards a Scaling Law for Large-Scale Recommendation
• [x] TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
• CAN: Feature Co-Action for Click-Through Rate Prediction
• DPN: Deep Pattern Network for Click-Through Rate Prediction
• MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
• Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
• AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction
• DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
• AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction
• TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
• End-to-end training of Multimodal Model and ranking Model
• Unified Visual Preference Learning for User Intent Understanding
• Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
• NON/FINT: https://github.com/anonctr/GDCN/tree/main/models