ranking

Best Practices on Recommendation Systems

⭐ Web frameworks for Go, most starred on GitHub

Learning to Rank in TensorFlow

Multi-Task Deep Neural Networks for Natural Language Understanding

A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.

🕷️Github 中国和全球用户排名，全球仓库 Star 最多排名(Github Action自动日更)。

A low code Machine Learning service that personalizes articles, listings, search results, recommendations to boost user engagement. A friendly Learn-to-Rank engine

DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks

⭐Github Ranking⭐ Github stars and forks ranking list. Github Top100 stars list of different languages. Automatically update daily. | Github仓库排名，每日自动更新

Awesome Search - this is all about the (e-commerce) search and its awesomeness

Fast, differentiable sorting and ranking in PyTorch

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

🎶 The ultimate community-driven, and open-source competitive rhythm game available on Steam.

Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems

Fast Differentiable Sorting and Ranking

专注于解决推荐领域与搜索领域的两个核心问题:排序预测(Ranking)和评分预测(Rating). 为相关领域的研发人员提供完整的通用设计与参考实现. 涵盖了70多种排序预测与评分预测算法,是最快最全的Java推荐与搜索引擎.

A Collection of BM25 Algorithms in Python

Learning to Rank in PyTorch

“Chorus” of recommendation models: a light and flexible PyTorch framework for Top-K recommendation.

An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"

multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.

A general purpose recommender metrics library for fair evaluation.

ORMs for Go, most starred on Github.

Deep Recommenders

PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models (IN PROGRESS)

Image search engine

A tensorflow implementation of Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

ElasticCTR，即飞桨弹性计算推荐系统，是基于Kubernetes的企业级推荐系统开源解决方案。该方案融合了百度业务场景下持续打磨的高精度CTR模型、飞桨开源框架的大规模分布式训练能力、工业级稀疏参数弹性调度服务，帮助用户在Kubernetes环境中一键完成推荐系统部署，具备高性能、工业级部署、端到端体验的特点，并且作为开源套件，满足二次深度开发的需求。

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