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Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR/CVR prediction), Post Ranking, Large Model (Generative Recommen…
A PyTorch Library for Multi-Task Learning
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.
Awesome Multitask Learning Resources
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）
A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io
AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
Python library for converting Scikit-Learn pipelines to PMML
Easy converter pandas -> tfrecords & tfrecords -> pandas
What is CLV or LTV? CLV or LTV is a metric that helps you measure the customer's lifetime value to a business. In this kernel, I am sharing the customer lifetime value prediction using BG-NBD, Pare…
A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.
上海交通大学 LaTeX 论文模板 | Shanghai Jiao Tong University LaTeX Thesis Template
PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
RSTutorials: A Curated List of Must-read Papers on Recommender System.
List of tools & datasets for anomaly detection on time-series data.
MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
A curated list of awesome Recommender System (Books, Conferences, Researchers, Papers, Github Repositories, Useful Sites, Youtube Videos)
Paper List for Recommend-system PreTrained Models
Loss function which directly targets ROC-AUC
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
A game theoretic approach to explain the output of any machine learning model.
the CS missing semester Chinese version
Probabilistic time series modeling in Python
Sequence modeling benchmarks and temporal convolutional networks
A curated, but incomplete, list of data-centric AI resources.
scikit-learn: machine learning in Python