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FlicksMAB is a movie recommendation system that leverages the power of multi-armed bandits (MAB) to personalize movie suggestions for users. Built using PyTorch, this system uses the MovieLens 100K dataset to learn user preferences and recommend movies that are likely to engage them.
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
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.
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.
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.