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a simple pytorch implement of Multi-Sample Dropout
Official code implementation for ICDE 23 paper MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/
This is a repository of the experimental code supporting the paper Multi-task Learning for CTR Prediction and Market Price Modeling in Real-time Bidding Advertising. The source code and datasets wi…
MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch. 「妙计包」是一个包含50+点击率预估和推荐系统深度模型的、通过TensorFlow和PyTorch撰写的库。
Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832
[WSDM 2023]Towards Universal Cross-Domain Recommendation
Source code for paper: Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System
[AAAI 2024] The open source code for "STEM: Unleashing the Power of Embeddings for Multi-Task Recommendation".
About Code Release for "On the Embedding Collapse When Scaling Up Recommendation Models" (ICML 2024)
Source code for Twitter's Recommendation Algorithm
Experiment results using FM, FFM and DeepFM algorithms in Criteo Display Advertising Challenge(https://www.kaggle.com/c/criteo-display-ad-challenge) dataset
搜索、推荐、广告、用增等工业界实践文章收集（来源：知乎、Datafuntalk、技术公众号）
scikit-learn: machine learning in Python
Reliability diagrams, Platt's scaling, isotonic regression
😘 让你“爱”上 GitHub，解决访问时图裂、加载慢的问题。（无需安装）
Google Research
A PyTorch Library for Multi-Task Learning
ZotCard is a plug-in for Zotero, which is a card note-taking enhancement tool. It provides card templates (such as concept card, character card, golden sentence card, etc., by default, you can cust…
Everything about note management. All in Zotero.
Zotero is a free, easy-to-use tool to help you collect, organize, annotate, cite, and share your research sources.
pytorch open-source library for the paper "AdaTT Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations"
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.