https://github.com/pwc-1/Paper-9/tree/main/4/SmeLU-master
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
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/qiaoguan/deep-ctr-prediction
https://github.com/qibinc/RippleNet-PyTorch
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
https://github.com/qmonmous/BigData-X-Python
https://github.com/qmonmous/pySpark-X-RecommenderSys--intro
https://github.com/recommenders-team/recommenders
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
https://github.com/reczoo/FuxiCTR/tree/main/model_zoo/EulerNet
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
https://github.com/reczoo/FuxiCTR/tree/main/model_zoo/LongCTR
https://github.com/reczoo/recsys2024_ctr_challenge
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
https://github.com/renmada/PaddleRec/tree/autofis/models/rank/autofis
https://github.com/samiwilf/dlrm_from_shz0116
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
https://github.com/sandeepnair2812/Deep-Learning-Based-Search-and-Recommendation-System
https://github.com/saulhazelius/deepFM
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
https://github.com/sdu-wjh/icws2020
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
https://github.com/sea-wyq/Fat-deepFFM
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
https://github.com/searchlink/din
https://github.com/sfu-db/feataug
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
https://github.com/shenweichen/DeepCTR-PyTorch
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
https://github.com/shichence/AutoInt
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
https://github.com/shiwang0211/kdd_cup_2019
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
https://github.com/simuler/DCN-V2
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
https://github.com/soycoder/dlrm-fb
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
https://github.com/sparsh-ai/RecommenderSystems
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
https://github.com/tangxyw/RecAlgorithm
https://github.com/tensorflow/models/tree/master/official/recommendation/ranking
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
https://github.com/tezignlab/RippleNet-TF2
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
https://github.com/thinkall/deepfefm
Field-Embedded Factorization Machines for Click-through rate prediction
https://github.com/tinyclues/group-by-augmentations-model/blob/master/training/layers.py
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
https://github.com/tuanio/AutoRec
https://github.com/twitter/the-algorithm
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
https://github.com/vinaymittal22/Income_Prediction_US
https://github.com/wangweitong/DL
https://github.com/wangweitong/recommend_system
https://github.com/wangzhongruo/news_cards_recommender
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
https://github.com/wenjiewwj/clickbait
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
https://github.com/whw199833/gbiz_torch/blob/main/example/model/ContextNetModel_test.py
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.