Popular GitHub Repositories for Learning To Rank

A Python implementation of LightFM, a hybrid recommendation algorithm.

Deep recommender models using PyTorch.

Learning to Rank in TensorFlow

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

ICCV 2019 (oral) RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution. PyTorch implementation

利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.

My (slightly modified) Keras implementation of RankNet and PyTorch implementation of LambdaRank.

Code for WWW'19 "Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm", which is based on LightGBM

A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.

Context-sensitive ranking and choice in Python with PyTorch

A deep reinforcement learning approach to search engine ranking (PyTorch). Final Project for UC Berkeley's CS 285: Deep Reinforcement Learning, Decision Making, and Control

The code repository for "Model Spider: Learning to Rank Pre-Trained Models Efficiently"

Solution to kaggle competition OTTO – Multi-Objective Recommender System: https://www.kaggle.com/competitions/otto-recommender-system

Active Learning for Learning to Rank (LETOR)

Python Machine Learning Library

?A TensorFlow Lite implementation of Google NIMA (Neural Image Assessment)

? Algolia search ML experiments on Hacker News data

A pipeline that collects, preprocesses and labels raw user-item interaction data to build a recsys with collaborative filtering (SVD, ALS) and LTR (XGBoost ranking) methods

Information retrieval engine and machine learning ranking.

Using NLP techniques (word and sentence embedding tools like SBERT and Learning-to-Rank systems like RankNet and LambdaRank) to rank candidates.