Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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Updated
Jun 1, 2024 - Rust
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
A recommender system for GitHub repositories based on Gorse
Gorse open source recommender system engine
This is a Recommender System built to suggest movies with content based and collaborative filtering with help of machine learning.
ColdRec: A Comprehensive Benchmark for Cold-Start Recommendation.
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.
Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍
Sistem Rekomendasi Posisi Lowongan Magang pada program MSIB Kampusmerdeka
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
Repository of recommender systems data and model information
Recommender system using XGBOOST, Neural_Network, Ensemble and LGBM
Pytorch domain library for recommendation systems
Additional utils and helpers to extend TensorFlow when build recommendation systems, contributed and maintained by SIG Recommenders.
Featrix Open Source
📽️ Intuitive Movie Recommendations for You! 🍿
[Tobig's 컨퍼런스] VLM 모델을 활용한 대화형 코디 추천 시스템
The FranKGraphBench is a Framework to allow KG Aware RSs to be benchmarked in a reproducible and easy to implement manner. It was first created on Google Summer of Code 2023 for Data Integration between DBpedia and some standard RS datasets in a reproducible framework.
recurrent neural network based recommender systems @ ACM RecSys 24, SIGCHI
RecTools - library to build Recommendation Systems easier and faster than ever before
Official implementation of "Graph Signal Diffusion Model for Collaborative Filtering" (SIGIR 2024)
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