https://github.com/fuyuanlyu/OptInter
공식 코드Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
Papers with Code 아카이브
Click-Through Rate Prediction
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Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.
<span style="color:grey; opacity: 0.6">( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fuyuanlyu/optembed
공식 코드OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
https://github.com/fuyuanlyu/optfs
공식 코드https://github.com/guotong1988/Automatic-Label-Error-Correction
공식 코드https://github.com/hsliu-initial/rok
공식 코드Retrieval-Oriented Knowledge for Click-Through Rate Prediction
https://github.com/hwwang55/KGCN
공식 코드Knowledge Graph Convolutional Networks for Recommender Systems
https://github.com/hwwang55/MKR
공식 코드Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
https://github.com/hwwang55/RippleNet
공식 코드RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
https://github.com/intelligent-machine-learning/dlrover
공식 코드Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction
https://github.com/intelligent-machine-learning/tfplus
공식 코드Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction
https://github.com/justarter/flip
공식 코드https://github.com/jyonn/cage
공식 코드https://github.com/jyonn/cove
공식 코드https://github.com/jyonn/semantictokenizer
공식 코드https://github.com/kahno/feature_embedding
공식 코드https://github.com/kexin-kxzhang/optfusion
공식 코드Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
https://github.com/knife982000/recsys2023challenge
공식 코드Weighted Multi-Level Feature Factorization for App ads CTR and installation prediction
https://github.com/lattice-zjj/mmctr_code
공식 코드Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge
https://github.com/lordzth666/WWW-23_NASRec
공식 코드NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
https://github.com/lpworld/DASL
공식 코드Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
https://github.com/mercari/mercari-ml-merrec-pub-us
공식 코드MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems
https://github.com/mitao-cat/feature-level_bias
공식 코드Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
https://github.com/mogicianxd/resus
공식 코드RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
https://github.com/mouna99/dien
공식 코드Deep Interest Evolution Network for Click-Through Rate Prediction
https://github.com/niuchenglei/ssm-dnn
공식 코드Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
https://github.com/niuchenglei/usm-dnn
공식 코드Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
https://github.com/nus-hpc-ai-lab/helen
공식 코드Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization
https://github.com/openbenchmark/BARS
공식 코드https://github.com/outbrain/outrank
공식 코드https://github.com/oywtece/deepmcp
공식 코드Representation Learning-Assisted Click-Through Rate Prediction
https://github.com/oywtece/dstn
공식 코드Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
https://github.com/oywtece/gme
공식 코드Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
https://github.com/oywtece/minet
공식 코드MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
https://github.com/pepsi2222/cela
공식 코드CELA: Cost-Efficient Language Model Alignment for CTR Prediction
https://github.com/pinskyrobin/www2025_mmctr
공식 코드1$^{st}$ Place Solution of WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction Challenge
https://github.com/project-basileus/multitype-sequence-generation-by-tlstm-gan
공식 코드Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.