Papers with Code 아카이브

Click-Through Rate Prediction

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하위 태스크: 0
평가표: 20
보존된 평가 기록: 127

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/Atomu2014/KNI

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An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

https://github.com/Atomu2014/product-nets

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Product-based Neural Networks for User Response Prediction

https://github.com/Atomu2014/product-nets-distributed

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Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

https://github.com/CRIPAC-DIG/Fi_GNN

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Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction

https://github.com/DeepGraphLearning/RecommenderSystems

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AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

https://github.com/Feiyang/MetaEmbedding

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Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings

https://github.com/Jinjiarui/GraphHINGE

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GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network

https://github.com/Kenyaggel/MLoRA

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MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization

https://github.com/Leavingseason/xDeepFM

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xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

https://github.com/Shereen-Elsayed/DeepMR

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Deep Multi-Representation Model for Click-Through Rate Prediction

https://github.com/UIC-Paper/MIMN

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Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

https://github.com/WayneDW/sDeepFwFM

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DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving

https://github.com/YaoYao1995/i-Razor

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i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems

https://github.com/aaronpanxiaofeng/fan

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FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation

https://github.com/aaronpanxiaofeng/moef

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MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction

https://github.com/aimetrics/jarvis

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FLEN: Leveraging Field for Scalable CTR Prediction

https://github.com/alibaba/tgin

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Triangle Graph Interest Network for Click-through Rate Prediction

https://github.com/alimama-creative/AutoCO

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Automated Creative Optimization for E-Commerce Advertising

https://github.com/ancientshi/karsein

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CTR-KAN: KAN for Adaptive High-Order Feature Interaction Modeling

https://github.com/anonctr/GDCN

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Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction

https://github.com/archersama/uni-ctr

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A Unified Framework for Multi-Domain CTR Prediction via Large Language Models

https://github.com/bayi-hu/gift-graph-guided-feature-transfer-network

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GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction

https://github.com/bigdata-ustc/XCrossNet

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XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction

https://github.com/bytedance/largebatchctr

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CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU

https://github.com/chiangel/map-code

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MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction

https://github.com/chrisliiiii/swan

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Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction

https://github.com/cl4ctr/cl4ctr

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CL4CTR: A Contrastive Learning Framework for CTR Prediction

https://github.com/cloudcatcher888/fesail

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Feature Staleness Aware Incremental Learning for CTR Prediction

https://github.com/codectr/refinectr

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A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction

https://github.com/cripac-dig/destine

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Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

https://github.com/ct4rec/ct4rec

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CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation

https://github.com/duchao0726/DUAL

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Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning

https://github.com/ezailshen/www-22-dihn

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Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation

https://github.com/facebookresearch/dlrm

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Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

https://github.com/facebookresearch/nasrec

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NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

https://github.com/frnetnetwork/frnet

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Enhancing CTR Prediction with Context-Aware Feature Representation Learning

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