Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models

You can train a neural network with user ratings or purchases, and use it to make recommendations; deep learning can be very good at recognizing patterns in a way similar to how our brain may do it. It's good at things like image recognition and predicting sequences of events.Neural networks are fundamentally matrix operations and there are already well-established matrix factorization techniques for recommender systems that fundamentally do something similar. In SVD for example, we find matrices that we multiply together using weights that are learned from stochastic gradient descent, it's almost the same thing, just thought of in a different way. So yeah, you could think of recommender systems as looking for patterns, just very complex ones based on the behavior of other people. So a matrix factorization can be modeled as a neural network. I think the main reason to experiment with applying neural networks to recommender systems is that it lets us take advantage of all the rapid advances in the fields of AI and deep learning. Amazon, for example, has open-sourced a system called DSSTNE, that's D-S-S-T-N-E, which allows you to run huge neural networks that deal with sparse data, on a cluster, efficiently. They claim to be using this internally for their own recommender systems. There are also ways to use TensorFlow in a cluster, and take advantage of a whole fleet of GPUs. And there's always research on new topologies for neural networks that can lead to fresh insights on how to make better recommendations using them. In some cases, approaches using neural networks have been shown to outperform SVD already, even if it's by a rather small margin. So, let's dive into some ways you can apply neural networks to the problem of making recommendations.

Evaluating ranking accuracy of Amazon's DSSTNE

DSSTNE Starter is easy to help making a sample project of Deep Learning based on Amazon DSSTNE and AWS G2 Instances.

Learning objectives Top-N recommender architectures Types of recommenders Python basics for working with recommenders Evaluating recommender systems Measuring your recommender Reviewing a recommender engine framework Content-based filtering Neighborhood-based collaborative filtering Matrix factorization methods Deep learning basics Applying deep learning to recommendations Scaling with Apache Spark, Amazon DSSTNE, and AWS SageMaker Real-world challenges and solutions with recommender systems Case studies from YouTube and Netflix Building hybrid, ensemble recommenders

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Evaluation Framework and Amazon DSSTNE

samples around Amazon's DSSTNE

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This is improved recommendation system which uses amazon's DSSTNE neural Network

A walk-through guide to install Amazon DSSTNE on the local machine using Docker.

Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) …