recommender-system

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.

AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding

推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/

RSTutorials: A Curated List of Must-read Papers on Recommender System.

A Python implementation of LightFM, a hybrid recommendation algorithm.

Papers on Computational Advertising

Fast Python Collaborative Filtering for Implicit Feedback Datasets

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

Classic papers and resources on recommendation

Accelerated deep learning R&D

Deep recommender models using PyTorch.

📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.

This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

A Deep Learning Recommender System

🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .

Pytorch domain library for recommendation systems

Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Pre-Ranking, Ranking, Post Ranking, Relevance, LLM and RL. Please cite our paper "Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives" (TOIS 2026).