Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
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Updated
May 14, 2024 - Python
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
in this section will be user based recommender on movies and ratings dataset
easy use user based collaborative filtering recommender system
This library is a PHP implementation of the collaborative filtering (CF).
Recommendation System for Appliances, along with Topic Modelling and Sentiment Analysis
Book Recommendation Service
Recommendation algorithms
Using the MovieLens 20 Million review dataset, this project aims to explore different ways to design, evaluate, and explain recommender systems algorithms. Different item-based and user-based recommender systems are showcased as well as a hybrid algorithm using a modified page-rank algorithm.
Book recommendation system using user base collaborative filter Algorithm and testing the accuracy result by comparing with different algorithms
The site offers movie recommendations based on user and item-based collaborative filtering, utilizing other users' ratings to provide personalized suggestions on the website.
Building a collaborative filtering recommender systems on books dataset
Demo is available at https://huggingface.co/spaces/quyanh/Book-Recommender-System
TMDB_5000_Movie_recommendation_system is a repository for a hybrid movie recommendation system. Discover personalized movie recommendations based on user preferences and movie features using the TMDB 5000 Movies dataset.
Create A Recommendation Engine For Blog Articles
Recommendation System for an Online Beer Company
A python implementation of a hybrid semantic-based collaborative filtering recommender systems.
Used User-based and Item-based Collaborative Filtering techniques to build a personalized Book Recommendation System
Collaborative recommendation engine model for product similarity estimation
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