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Recommenders

The Microsoft Recommenders repository on github provides examples and best practices for building recommendation systems. This package provides a demonstration of using the restricted Boltzmann machine (RBM) algorithm for building a recommendation engine.

The hard work is done using the utilities provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting train/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications.

The MovieLens data sets are used in this demonstration, containing 100,004 ratings across 9125 movies created by 671 users between 9 January 1995 and 16 October 2016. The dataset records the userId, movieId, rating, timestamp, title, and genres. The goal is to build a recommendation model to recommend new movies to users.

Usage

MLHub is a command line utility to quickly demonstrate the capabilities of pre-built machine learning models and data science processes. Visit MLHub.ai for details.

To install and run the pre-built scripts:

$ pip3 install mlhub
$ ml install   rbm
$ ml configure rbm
$ ml demo      rbm
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