A Python implementation of LightFM, a hybrid recommendation algorithm.

An index of algorithms for learning causality with data

Deep recommender models using PyTorch.

Learning to Rank in TensorFlow

Awesome Search - this is all about the (e-commerce, but not only) search and its awesomeness

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

A machine learning tool that ranks strings based on their relevance for malware analysis.

Learning to Rank in PyTorch

Python learning to rank (LTR) toolkit

The codebase for the book "AI-Powered Search" (Manning Publications, 2025)

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.

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

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

Tensorflow implementations of various Learning to Rank (LTR) algorithms.

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

train models in pytorch, Learn to Rank, Collaborative Filter, Heterogeneous Treatment Effect, Uplift Modeling, etc

Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data

Must-read Papers for Recommender Systems (RS)

The repository for 'Uncertainty-aware blind image quality assessment in the laboratory and wild' and 'Learning to blindly assess image quality in the laboratory and wild'

Code for CVPR 2019 paper "Deep Metric Learning to Rank"

This is official Pytorch code and datasets of the paper "Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News", EMNLP 2020.

Official repository of RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions.

Reference Implementation for WSDM 2018 Paper "Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering"

Context-sensitive ranking and choice in Python with PyTorch

CVPR 2019: Ranked List Loss for Deep Metric Learning, with extension for TPAMI submission

My most frequently used learning-to-rank algorithms ported to rust for efficiency. Try it: "pip install fastrank".

An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow implementation of SERank model. The code is developed based on TF-Ranking.

Implementation of SetRank in SIGIR 2020

tools for fast reading of docs

Code for CVPR 2018 paper "Hashing as Tie-Aware Learning to Rank"