OpenCTR

Click-through rate (CTR) prediction is an important task in many industrial applications such as online advertising, recommender systems, and sponsored search. OpenCTR builds an open-source library for benchmarking existing CTR prediction models.

Model List

CTR prediction models currently available:

Publication	Model	Paper	Available
WWW'07	LR	Predicting Clicks: Estimating the Click-Through Rate for New Ads [Microsoft]	:heavy_check_mark:
ICDM'10	FM	Factorization Machines	:heavy_check_mark:
CIKM'15	CCPM	A Convolutional Click Prediction Model	:heavy_check_mark:
RecSys'16	FFM	Field-aware Factorization Machines for CTR Prediction [Criteo]	:heavy_check_mark:
RecSys'16	YoutubeDNN	Deep Neural Networks for YouTube Recommendations [Google]	:heavy_check_mark:
DLRS'16	Wide&Deep	Wide & Deep Learning for Recommender Systems [Google]	:heavy_check_mark:
ECIR'16	FNN	Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction [RayCloud]	:heavy_check_mark:
ICDM'16	IPNN	Product-based Neural Networks for User Response Prediction	:heavy_check_mark:
KDD'16	DeepCross	Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features [Microsoft]	:heavy_check_mark:
NIPS'16	HOFM	Higher-Order Factorization Machines	:heavy_check_mark:
IJCAI'17	DeepFM	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, [Huawei]	:heavy_check_mark:
SIGIR'17	NFM	Neural Factorization Machines for Sparse Predictive Analytics	:heavy_check_mark:
IJCAI'17	AFM	Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks	:heavy_check_mark:
ADKDD'17	DCN	Deep & Cross Network for Ad Click Predictions [Google]	:heavy_check_mark:
WWW'18	FwFM	Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising [Oath, TouchPal, LinkedIn, Ablibaba]	:heavy_check_mark:
KDD'18	xDeepFM	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems [Microsoft]	:heavy_check_mark:
KDD'18	DIN	Deep Interest Network for Click-Through Rate Prediction [Alibaba]	:heavy_check_mark:
CIKM'19	FiGNN	FiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction	:heavy_check_mark:
CIKM'19	AutoInt+	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks	:heavy_check_mark:
RecSys'19	FiBiNET	FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction [Sina Weibo]	:heavy_check_mark:
WWW'19	FGCNN	Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction [Huawei]	:heavy_check_mark:
AAAI'19	HFM+	Holographic Factorization Machines for Recommendation	:heavy_check_mark:
NeuralNetworks'20	ONN	Operation-aware Neural Networks for User Response Prediction	:heavy_check_mark:
AAAI'20	AFN+	Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions	:heavy_check_mark:
AAAI'20	LorentzFM	Learning Feature Interactions with Lorentzian Factorization	:heavy_check_mark:
WSDM'20	InterHAt	Interpretable Click-through Rate Prediction through Hierarchical Attention	:heavy_check_mark:
DLP-KDD'20	FLEN	FLEN: Leveraging Field for Scalable CTR Prediction [Tencent]	:heavy_check_mark:

Metadata

Release files for openctr 0.1.0

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Built distribution (wheel)

Table of built distributions (wheels) for openctr 0.1.0
File	Interpreter	ABI	Platform	Reset
openctr-0.1.0-py3-none-any.whl 41.8 kB Sep 29, 2021	Python 3	none	any	Details

Release files / openctr-0.1.0-py3-none-any.whl

Download URL	openctr-0.1.0-py3-none-any.whl
Size	41.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`15374a9d768daaf7ff248409e2d015bd8b87ab367f13270203d6248f6268208c`
BLAKE2b-256 checksum How to use checksums	`3eb6114c5c89e41f2adf7a4ddb0c6f0d74aab77335cd2640bd347bd02a356696`
Upload date	Sep 29, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.4.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.6.5`

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0.1.0 This release

Sep 29, 2021 1 release file

openctr 0.1.0

OpenCTR

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