MovieRecEngine be an abbreviation of Movie Recommendation Engine. This is a simple collaborative filtering based library using Pytorch Sequential Neural Network to make your Movie Recommendation System easy.

This library is in very early-stage currently! So, there might be remarkable changes.

Use the package manager pip to install MovieRecEngine.

pip install MovieRecEngine

MovieRecEngine 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.

MovieRecEngine, uses tez simple pytorch trainer that supports cpu and gpu training.

• To train a model using MovieRecEngine, define a Dataset that contains columns "userId", "movieId", "ratings". Example Train sample

• Create a object for Train class in MovieRecEngine library with parameters trainDatasetPath, userLabelEncoderPath, movieLabelEncoderPath, validDatasetSize, trainBatchSize, validBatchSize, device, nEpochs, trainedModelPath, randomState.

• Train the model by calling train function in Train class.

• To predict user movie ratings using MovieRecEngine, define a Dataset that contains columns "userId", "movieId", "ratings". Example Predict sample

NOTE: "userId" needs to contain 1 unique userId.

• Create a object for Predict class in MovieRecEngine library with parameters datasetPath, userLabelEncoderPath, movieLabelEncoderPath, trainedModelPath, predictBatchSize, device.
• Predict user movie ratings by calling predict function in Predict class.

1. Train class:

• trainDatasetPath ==> Path for your training Dataset.
• userLabelEncoderPath ==> Path in which you want to save user Label Encoder (this will be used in your prediction)
• movieLabelEncoderPath ==> Path in which you want to save movie Label Encoder (this will be used your prediction)
• validDatasetSize ==> Test size for train_test_split
• trainBatchSize ==> The number of train samples to work through before updating the internal model parameters.
• validBatchSize ==> The number of test samples to work through before updating the internal model parameters.
• device ==> Device in which you want to train your model 'cuda' or 'cpu'. Default 'cpu'.
• nEpochs ==> The number times that the learning algorithm will work through the entire training dataset.
• trainedModelPath ==> Path to save your trained model (this will be used in your prediction)
• randomState ==> Random State values for train_test_split

2. Predict class:

• datasetPath ==> Path for your prediction Dataset.
• userLabelEncoderPath ==> Path in which you saved user Label Encoder (while training)
• movieLabelEncoderPath ==> Path in which you saved movie Label Encoder (while training)
• trainedModelPath ==> Path in which you saved Trained model (while training)
• predictBatchSize ==> The number of prediction samples to work
• device ==> Device in which you want to train your model 'cuda' or 'cpu'. Default 'cpu'.

Currently, we are not accepting any pull requests! All PRs will be closed. If you want a feature or something doesn't work, please create an issue.