GitHub topics: click-through-rate-prediction
fjk1533/smurphcast
SmurphCast – percentage‑first time‑series forecasting (churn, CTR, conversion, retention) with additive + GBM + ES‑RNN stacking and automatic model selection. 100 % Python, CPU‑friendly, explainable.
Language: Python - Size: 69.3 KB - Last synced at: 1 day ago - Pushed at: 1 day ago - Stars: 1 - Forks: 0
Halsted312/smurphcast
SmurphCast – percentage‑first time‑series forecasting (churn, CTR, conversion, retention) with additive + GBM + ES‑RNN stacking and automatic model selection. 100 % Python, CPU‑friendly, explainable.
Language: Python - Size: 85 KB - Last synced at: 3 days ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0
chenxingqiang/DNN-YouTube-RecSys
This repository provides a comprehensive implementation of a deep neural network-based recommendation system similar to YouTube's. The repo is organized to include the core Python implementation of the model and a Spark-based Scala solution for data generation and model serving.
Language: Python - Size: 1.86 MB - Last synced at: 3 months ago - Pushed at: 9 months ago - Stars: 54 - Forks: 32
thaychansy/click-through-rate-prediction
Click-Through Rate (CTR) prediction is a crucial task in online advertising aimed at estimating the likelihood of a user clicking on an ad.
Language: Jupyter Notebook - Size: 499 KB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0
anhtngc/IS252-ClickThroughRatePrediction Fork of LoylP/Data_Mining_App
a course project calculated as the number of clicks an ad receives divided by the number of times the ad is shown (impressions), expressed as a percentage.
Language: Jupyter Notebook - Size: 4.49 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0
zahrahmerchant/Click-Through-Rate-Analysis
This project analyzes click-through rates (CTR) for advertising campaigns using a dataset of ad impressions and clicks. The goal is to derive insights and improve advertising strategies based on the analysis.
Language: Jupyter Notebook - Size: 314 KB - Last synced at: 3 days ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0
GNOEYHEAT/CTR_stacking
웹 광고 클릭률 예측 AI 경진대회, DACON (2024.05.07 ~ 2024.06.03)
Language: Python - Size: 556 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 1
qwerfdsaplking/SATrans
The source code for our paper "Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction" (accepted by KDD2023 Applied Science Track), which proposes a model for Multi-Scenario/Multi-Domain Recommendation.
Language: Python - Size: 155 KB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 27 - Forks: 4
QinHsiu/DeepRecCode
This respository is used to log some deep learning based recommendation models.
Language: Python - Size: 701 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0
BinFuPKU/CTRRecommenderModels
I have surveyed the technology and papers of CTR & Recommender System, and implemented 25 common-used models with Pytorch for reusage. （对工业界学术界的CTR推荐调研并实现25个算法模型，2023）
Language: Jupyter Notebook - Size: 12.2 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0
Bayi-Hu/GIFT-Graph-guided-Feature-Transfer-Network
Source code for GIFT (CIKM 22)
Language: Python - Size: 15.1 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 9 - Forks: 3
guotong1988/criteo_dataset
Get AUC 0.809 at Criteo dataset by MLP
Language: Python - Size: 36.1 KB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0
guotong1988/movielens_dataset
Get AUC 0.794 at Movielens 20M dataset
Language: Python - Size: 37.1 KB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0