#Recommendation-engine
Showing 18 of 18 repositories tagged #recommendation-engine, ranked by stars
Best Practices on Recommendation Systems
Deep learning for recommender systems
Machine Learning Platform and Recommendation Engine built on Kubernetes
A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
Not an Amazon-style catalog or marketplace. ctx is a recommendation layer: bring your org tools or use the shipped graph to load the right skills, agents, MCPs, and harnesses only for the current dev window, cutting token bills and local compute waste: 79,958-node LLM-wiki graph, 68,494 skills, 467 agents, 10,790 MCPs, 207 harnesses.
RecTools - library to build Recommendation Systems easier and faster than ever before
recommender system tutorial with Python
Machine learning for beginner(Data Science enthusiast)
A curated list of repositories for my book Machine Learning Solutions.
This repository contains the code for building movie recommendation engine.
⚡️ Implementation of TRON: Transformer Recommender using Optimized Negative-sampling, accepted at ACM RecSys 2023.
Predictive memory layer for AI agents. MongoDB + Qdrant + Neo4j with multi-tier caching, custom schema support & GraphQL. 91% Stanford STARK accuracy, <100ms on-device retrieval
🤹 MultiTRON: Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems, accepted at ACM RecSys 2024.
Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.
Movie Recommendation System: Project using R and Machine learning
Movie Recommendation Chatbot provides information about a movie like plot, genre, revenue, budget, imdb rating, imdb links, etc. The model was trained with Kaggle’s movies metadata dataset. To give a recommendation of similar movies, Cosine Similarity and TFID vectorizer were used. Slack API was used to provide a Front End for the chatbot. IBM Watson was used to link the Python code for Natural Language Processing with the front end hosted on Slack API. Libraries like nltk, sklearn, pandas and nlp were used to perform Natural Language Processing and cater to user queries and responses.
This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.
A Recommendation Engine API that can be used to recommend movies, music, games, manga, anime, comics, tv shows and books. Deployed using an AWS EC2 instance.