Papers with Code 아카이브

Click-Through Rate Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/psycho-demon/mirrn

공식 코드

Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction

https://github.com/ptzhangAlg/RecAlg

공식 코드

MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction

https://github.com/qinjr/UBR4CTR

공식 코드

User Behavior Retrieval for Click-Through Rate Prediction

https://github.com/qinjr/rim

공식 코드

Retrieval & Interaction Machine for Tabular Data Prediction

https://github.com/racp-submission/racp

공식 코드

Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search

https://github.com/ravikucb/dnas

공식 코드

Differentiable NAS Framework and Application to Ads CTR Prediction

https://github.com/recommendation-algorithm/fibinet

공식 코드

FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction

https://github.com/reczoo/BARS

공식 코드

BARS-CTR: Open Benchmarking for Click-Through Rate Prediction

https://github.com/reczoo/FuxiCTR

공식 코드

FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction

https://github.com/reczoo/RecZoo/tree/main/ranking/ctr/FinalMLP

공식 코드

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

https://github.com/rener1199/deep_memory

공식 코드

Click-Through Rate Prediction with the User Memory Network

https://github.com/rucaibox/dagfm

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Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation

https://github.com/rucaibox/eulernet

공식 코드

EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction

https://github.com/rucaibox/ufin

공식 코드

UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction

https://github.com/salmon1802/DCNv3

공식 코드

FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction

https://github.com/salmon1802/QNN

공식 코드

Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction

https://github.com/salmon1802/cetn

공식 코드

CETN: Contrast-enhanced Through Network for CTR Prediction

https://github.com/salmon1802/ektf

공식 코드

Ensemble Learning via Knowledge Transfer for CTR Prediction

https://github.com/salmon1802/qin

공식 코드

Quadratic Interest Network for Multimodal Click-Through Rate Prediction

https://github.com/salmon1802/tf4ctr

공식 코드

TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation

https://github.com/shenweichen/DSIN

공식 코드

Deep Session Interest Network for Click-Through Rate Prediction

https://github.com/shenweichen/DeepCTR

공식 코드

Field-Embedded Factorization Machines for Click-through rate prediction

https://github.com/shenweichen/DeepCTR-Torch

공식 코드

Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

https://github.com/skylerlinn/understanding-the-ranking-loss

공식 코드

Understanding the Ranking Loss for Recommendation with Sparse User Feedback

https://github.com/snap-research/graphhash

공식 코드

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems

https://github.com/stellaxu/DHAN

공식 코드

Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction

https://github.com/tensorflow/recommenders/blob/v0.5.1/tensorflow_recommenders/layers/feature_interaction/dcn.py

공식 코드

DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

https://github.com/thomasahle/cce

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Clustering the Sketch: A Novel Approach to Embedding Table Compression

https://github.com/thuwangcy/rechorus

공식 코드

ReChorus2.0: A Modular and Task-Flexible Recommendation Library

https://github.com/thyrixyang/courier

공식 코드

COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation

https://github.com/tsinghua-fib-lab/Causal-Recommender-Systems

공식 코드

Causal Inference in Recommender Systems: A Survey and Future Directions

https://github.com/ustc-starteam/dlf

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DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction

https://github.com/ustc-starteam/mec

공식 코드

A Universal Framework for Compressing Embeddings in CTR Prediction

https://github.com/ustc-starteam/mirrn

공식 코드

Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction

https://github.com/waldenlqy/sam

공식 코드

Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences

https://github.com/wnzhang/deep-ctr

공식 코드

Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction

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