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A Recommender System that predicts ratings from 1 to 5 on MovieLens 1M Dataset

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A Stacked AutoEncoder model with PyTorch

Note: This repository contains homework assignments for the Artificial Intelligence course at the University of Glasgow, created while I served as the assistant lecturer and held a postdoctoral position, focusing on Python, deep learning, and associated tools.

Self-Supervised Learning

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A Recommender System that predicts ratings from 1 to 5 on MovieLens 1M/100K Dataset

Download the dataset from: https://grouplens.org/datasets/movielens/

An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal noise. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name.

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A Recommender System that predicts ratings from 1 to 5 on MovieLens 1M Dataset

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