Recommender systems academic course project

A movie recommender. Collaborative and content based filtering hybrid model.

Factorization Machine models in PyTorch

An overview of reccomendation systems in Python

A recommendation algorithm implemented with Biased Matrix Factorization method using tensorflow and tested over 1 million Movielens dataset with state-of-the-art validation RMSE around ~ 0.83

Recommendation Systems (Collaborative Filtering) Experiments on MovieLens Datasets

implementation of a movie recommendation system using the K Nearest Neighbors algorithm. The system suggests movies based on user-provided personal information and rated movies.

Movie recommendation system using the 10 million MovieLens dataset.

Neural collaborative filtering recommendation system on Movie lens 100k dataset

Movie recommendation model on MovieLens dataset

Movie Recommendations over the MovieLens dataset using Matrix Factorization

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.

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MovieLens Recommendation System: A Python-based project that utilizes dimension reduction techniques and clustering algorithms to provide movie recommendations using a dataset of 100,000 MovieLens ratings.

Movies Recommendation Systems with Personalization

Rate movies and get recommendations.

It is a movie recommender web application which is developed using the Python.

Progetto per il corso di Data Analytics 2022/2023 Informatica Magistrale UniBO

Recommender systems on MovieLens data using explicit ratings, and curated implicit feedback data.