Tensorflow implementation of DeepFM for CTR prediction.
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Updated
Jun 10, 2018 - Python
Tensorflow implementation of DeepFM for CTR prediction.
Click-Through Rate Estimation for Rare Events in Online Advertising
some ctr model, implemented by PyTorch, such as Factorization Machines, Field-aware Factorization Machines, DeepFM, xDeepFM, Deep Interest Network
Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"
PyTorch Implementation of Deep Interest Network for Click-Through Rate Prediction
CS7CS4- Machine Learning- Recommendation Algorithm- Click Prediction- Kaggle Competition
Recommendation system implementation
An eXtensible Package of Deep Learning based Ranking Models for Large-scale Industrial Recommender System with Tensorflow
An introduction of a simple approach for CTR Anomaly Detection
Must-read Papers for Recommender Systems (RS)
Training pipeline using TFRecord files
Dataset and code for “Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction”
I went on a 5 days sprint of completing some of my previously started projects and i hope to have 4 project deployed at the end of the 5th day.
ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop an ecosystem to experiment, share, reproduce, and deploy in real-world in a smooth and easy way.
Here I demonstrate the performance difference between the Poisson and the classic bootstrap by estimating the confidence interval for the difference of CTRs of the two user groups
Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"
This repository contains a machine learning model for predicting customer click-through rate on ads. By analyzing user demographics and browsing behavior, the model aims to identify potential customers with a higher likelihood of clicking on ads.
LightCTR is a tensorflow 2.0 based, extensible toolbox for building CTR/CVR predicting models.
StrikePrick is your one-stop destination for exposing and overturning ineffective, outdated email marketing strategies. This repository offers a data-driven, humor-infused critique of commonly touted advice, using verified statistics to debunk myths and set the record straight. Designed for e-commerce brands and marketers.
The source code of NRCGI (Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction, CIKM2023).
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