Recommendations for Ruby and Rails using collaborative filtering
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Updated
May 23, 2024 - Ruby
Recommendations for Ruby and Rails using collaborative filtering
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.
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Additional utils and helpers to extend TensorFlow when build recommendation systems, contributed and maintained by SIG Recommenders.
Pytorch domain library for recommendation systems
HierarchicalKV is a part of NVIDIA Merlin and provides hierarchical key-value storage to meet RecSys requirements. The key capability of HierarchicalKV is to store key-value feature-embeddings on high-bandwidth memory (HBM) of GPUs and in host memory. It also can be used as a generic key-value storage.
A Comparative Framework for Multimodal Recommender Systems
Versatile End-to-End Recommender System
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍
Image-based furniture recommendation system for better product discovery.
Trill - a web app for finding music
Quotes Recommender Project - Part of the Data Integration Course in Winter Term 23/24
RecTools - library to build Recommendation Systems easier and faster than ever before
Ranks hotel to a user search on the likeliness that it will be booked
ai: basic movie recommender system
This project demonstrates a simple movie recommendation system using collaborative filtering techniques with Python and Pandas.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
A recommender system built from scratch using the collaboration filtering algorithm and NumPy library
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