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.

Tevatron - A flexible toolkit for neural retrieval research and development.

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

My solution to the book A Collection of Data Science Take-Home Challenges

Language-Agnostic SEntence Representations

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"

A toolkit for building dense retrievers with deep language models.

A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).

Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

Implementation and experiments of graph embedding algorithms.

Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.

A summary of must-read papers for Neural Question Generation (NQG)

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)

Resources for the "CTRLsum: Towards Generic Controllable Text Summarization" paper

Evaluation code for various unsupervised automated metrics for Natural Language Generation.

Learning to Rank in PyTorch

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

allRank is a framework for training learning-to-rank neural models based on PyTorch.

Python learning to rank (LTR) toolkit

Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

Unsupervised text tokenizer for Neural Network-based text generation.

Notebooks and examples on how to onboard and use various features of Amazon Personalize

Script to scrape the product details of any search input.

Facilitating the design, comparison and sharing of deep text matching models.

Elasticsearch with BERT for advanced document search.

Lime: Explaining the predictions of any machine learning classifier

An Open-Source Package for Knowledge Embedding (KE)

Officially maintained, supported by PaddlePaddle, including CV, NLP, Speech, Rec, TS, big models and so on.