ColdRec: An Open-Source Benchmark Toolbox 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​.

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.

workin

The goal of this project is to use Netflix data (7787, 12) to classify and group movies and shows into specific clusters. We will utilize techniques such as K-means clustering, Agglomerative clustering, and content-based recommendation systems to analyze the data and provide personalized suggestions to consumers based on their preferences

A Comparative Framework for Multimodal Recommender Systems

Using the MovieLens dataset (ml-10M100K) to build and compare recommender systems in R and Python. Algorithms: RANDOM, POPULAR, UBCF, RSVD, Content-based.

Heart Failure Management System

App that recommends insurance products based on popularity, content and user similarity.

book recommender system analyzes user preferences and behaviors to suggest personalized book recommendations, enhancing user engagement and satisfaction in selecting reading material.

Pytorch domain library for recommendation systems

Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural Networks, Transfer Learning, Reinforcement Learning, Self-supervised Learning and so on.

Knowledge Graph Construction and Recommender System Development of Tourism in Singapore

Recipe Genie is a recipe recommendation system that recommends recipes to users based on the ingredients they have at home.

recommender system using OpenAI API

The AMC Problem Trainer sources past AMC problems adaptively to improve practicing efficiency. It distinguishes itself from other web-based applications in its ability to evaluate users’ strengths and weaknesses with problem types and categories and consequently recommend such problems.

This is a book recommendation system based on item-based Collaborative Filtering memory-based model created using Flask.