Movieholic uses collaborative filtering to find similarities between users and items simultaneously to provide recommendations. This allows for serendipitous recommendations; that is, collaborative filtering models can recommend an item to user A based on the interests of a similar user B. Furthermore, the embeddings can be learned automatically, without relying on hand-engineering of features.
MovieRecEngine uses pyptorch sequential Neural Networks to train a model that can predict users rating for an unseen movie based on his/her past interests/ratings provided.