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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Spotlight Implicit Factorization 0.9

By: Outpace Systems Latest Version: 0.9

Product Overview

An implicit feedback matrix factorization model. Uses a classic matrix factorization approach, with latent vectors used to represent both users and items. Their dot product gives the predicted score for a user-item pair. The model is trained through negative sampling: for any known user-item pair, one or more items are randomly sampled to act as negatives (expressing a lack of preference by the user for the sampled item).

Key Data

By Outpace Systems
Type Algorithm
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Additional Resources

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Support Information

Spotlight Implicit Factorization 0.9

see attached example notebook

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