An eCommerce website developed in the Django web framework. It implements a content-based filtering recommendation system based on the user usage history, user profile and item profile.
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Updated
Apr 9, 2017 - Python
An eCommerce website developed in the Django web framework. It implements a content-based filtering recommendation system based on the user usage history, user profile and item profile.
Collaborative Filtering NN and CNN based recommender implemented with MXNet
Music recommender system with collaborative and content-based filtering
web log analytics, recommender systems, Graph analysis, content-based/collaborative filtering
Recommendation System for Amazon Alexa E-Commerce Application
This is an implementation where content based and collaborative filtering techniques are used for recommending movies.
A movie recommendation system using several content and collaborative based filtering techniques
Recommendation Systems thesis. This repository contains the development of the evaluation of three recommendations system methods: Collaborative Filtering, Content Based and Hybrid.
Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows
Recommender System 2019 Challenge PoliMi
Objective of the project is to build a hybrid-filtering personalized news articles recommendation system which can suggest articles from popular news service providers based on reading history of twitter users who share similar interests (Collaborative filtering) and content similarity of the article and user’s tweets (Content-based filtering).
Recommending movies to user using various Colaborative Filtering and Content Based Filtering.
Recommendation system tasks
Posts/Feeds recommendation engine based on content based and collaborative filtering methods
Movie Website built on python Django framework; Uses Content Based Predictive Model approach to predict similar movies based on the contents/genres similarities
Movie recommender system using pipeline hybridization of 2 recommender methods: Content-Based Filtering and Collaborative Filtering.
Comparison of performance evaluation of the baseline and hybrid recommendation systems using various metrics, to prove that hybrid systems perform better
Movie recommendation app using content-based filtering. Data provided by TMDb.
Movie recommendation system to find common movie interests among a group of people.
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