Question 22

Explanation

Correct option:

Factorization Machines

The Factorization Machines algorithm is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. It is an extension of a linear model that is designed to capture interactions between features within high dimensional sparse datasets economically. For example, in a click prediction system, the Factorization Machines model can capture click rate patterns observed when ads from a certain ad-category are placed on pages from a certain page-category. Factorization machines are a good choice for tasks dealing with high dimensional sparse datasets, such as click prediction and item recommendation.

Therefore, Factorization Machines algorithm is the right fit for the given use case so that the company can create individualized storefronts having product recommendations that match each user’s specific interests and tastes.

Incorrect options:

XGBoost - The XGBoost (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. The XGBoost algorithm performs well in machine learning competitions because of its robust handling of a variety of data types, relationships, distributions, and the variety of hyperparameters that you can fine-tune. You can use XGBoost for regression, classification (binary and multiclass), and ranking problems.

XGBoost is not the right fit to build recommendation system for the given use case.

Linear Learner - Linear models are supervised learning algorithms used for solving either classification or regression problems. For input, you give the model labeled examples (x, y). x is a high-dimensional vector and y is a numeric label. For binary classification problems, the label must be either 0 or 1. For multiclass classification problems, the labels must be from 0 to num_classes - 1. For regression problems, y is a real number. The Amazon SageMaker linear learner algorithm provides a solution for both classification and regression problems.

Linear Learner is not the right fit to build recommendation system for the given use case.

BlazingText - The Amazon SageMaker BlazingText algorithm provides highly optimized implementations of the Word2vec and text classification algorithms. The Word2vec algorithm is useful for many downstream natural language processing (NLP) tasks, such as sentiment analysis, named entity recognition, machine translation, etc. Text classification is an important task for applications that perform web searches, information retrieval, ranking, and document classification.

BlazingText is not the right fit to build recommendation system for the given use case.

Reference:

https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html