Recommenders

Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems.

Recommenders is a project under the Linux Foundation of AI and Data.

This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:
• Prepare Data: Preparing and loading data for each recommendation algorithm.
• Model: Building models using various classical and deep learning recommendation algorithms such as Alternating Least Squares (ALS) or eXtreme Deep Factorization Machines (xDeepFM).
• Evaluate: Evaluating algorithms with offline metrics.
• Model Select and Optimize: Tuning and optimizing hyperparameters for recommendation models.
• Operationalize: Operationalizing models in a production environment on Azure.

Several utilities are provided in recommenders to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the Recommenders documentation.

For a more detailed overview of the repository, please see the documents on the wiki page.

For some of the practical scenarios where recommendation systems have been applied, see scenarios.

Link: GitHub

Navigational hashtags: #armknowledgesharing #armrepo
General hashtags: #recsys #recommenders #recommendersystems #recommendation

@data_science_weekly