A recommender system is one that seeks to predict the "rating" or "preference" a user would give to an item.
Cosine Similarity Notebook
Since the one-hot representation of movies is too sparse, we can create a dense representation using Principal Component Analysis. On this dense representation, we can recommend similar movies using cosine similarity metric.
Truncated SVD Notebook
In this method, we create representations of both, movies and users by considering the top-n factors from their Singular Value Decompositions. Next, using the (movie, user) representaion pairs we can train a regression model to predict the corresponding rating value.
Restricted Boltzmann Machines Notebook
The Restricted Boltzmann Machine (RBM) is a special type of artificial neural network. Here, the RBM is trained using the Contrastive Divergence loss function to estimate the distribution of ratings given the movie ratings of a user.
SVD Matrix Factorisation Notebook
This method involves embedding movies in a vector space by using a stochastic estimation of Matrix Factorisation. The movie embedding can be considered a representation of the movie features and we can make recommendations using a similarity metric.
Probabilistic Matrix Factorization Repository
A Bayesian approach to factorizing the Ratings matrix using Variational Inference. As a result, each rating prediction is a Gaussian with its variance representing uncertainty.
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Movie Sentiment : Analysing a movie review's text to determine whether it is positive or negative. Find the repository here.
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Anime Finder : A cosine similarity based anime recommendation engine along with a web-based interface. Find the repository here.
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Book Recommendations : An experiment using the Truncated SVD method to recommend books. Find the notebook here.