Examples of different types of recommender systems.
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Updated
May 9, 2023 - Jupyter Notebook
Examples of different types of recommender systems.
Books recommendation system based on a hybrid approach of both content-based and collaborative filtering.
I built recommender systems for recommending products to user using Model-based recommendation system.
I created movie recommender system using content based filtering.
Collaborative filtering using SVD
This repository contains a Python script mf.py that implements Matrix Factorization for collaborative filtering. Collaborative filtering is a technique used in recommendation systems to predict user preferences by collecting information from many users. Matrix Factorization is one of the popular methods used in collaborative filtering.
A movie recommendation system, or a movie recommender system, is an ML-based approach to filtering or predicting the users' film preferences based on their past choices and behavior.
Built a movie recommender system using Movielens dataset using both content-based filtering approach and collaborative filtering method.
Movie Recommendation System using the 10M MovieLens dataset
Movie Recommendation System using Collaborative Method (User - User similarity , Item-Item similarity)
book recommendation engine
MovieLens 100K and MovieLens 1M recommender system
Recommender Systems Project
Demo is available at https://huggingface.co/spaces/quyanh/Book-Recommender-System
Recommendation Sytem to Predict Movies using python
- Collabrative Filtering Based Recomendation System and Popular Filtering based Recommendation System
Versão final do recommender desenvolvido no DEX4 da DNC
Neural matrix factorization movie recommender paired with image similarity in poster design
This project implements a magazine recommender system using Amazon review data from 2018 . The system calculates similarity scores between magazines and recommends the most similar magazines to the user.
Simple and user-friendly Python package for building recommendation systems based on PMF.
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