This repository provides a comprehensive implementation of a deep neural network-based recommendation system similar to YouTube's. The repo is organized to incl

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.

Source code for GIFT (CIKM 22)

Get AUC 0.809 at Criteo dataset by MLP

Get AUC 0.794 at Movielens 20M dataset

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.

This respository is used to log some deep learning based recommendation models.

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.

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

Performance analysis of DeepFM Recommender System on CTR Dataset

웹 광고 클릭률 예측 AI 경진대회, DACON (2024.05.07 ~ 2024.06.03)

datawhale&科大讯飞举办的学习挑战赛————“广告点击率预估挑战赛” Rank9 方案