You are a machine learning specialist for a retail clothing company. Your company receives a significant amount of its revenue from your retail website. Your marketing team wishes to implement a recommendation feature for your customers that uses online and in-store shopping patterns, user preferences, and overall fashion trends to give suggestions to your customers for items to purchase. Your dataset contains customer data such as demographics, prior visits, prior purchases, and location. Your task is to develop a machine learning model that uses the customer data to enhance the user’s experience with your website while also giving informed recommendations. Which option best suits your task?

Answer options:

A.Build a model that uses the Random Cut Forest (RCF) algorithm with a training dataset of customer data to identify patterns in the customer data.
B.Build a model based on collaborative filtering that leverages implicit feedback derived from user activity, such as clicks and views to identify patterns in the customer data.
C.Build a recurrent neural network (RNN) using the DeepAR algorithm with an initial weight decay coefficient that adds L2 regularization and a minimum of five layers to identify patterns in the customer data.
D.Build a model using the Neural Topic Model (NTM) algorithm using the Stochastic Gradient Descent (SGD) optimizer to identify patterns in the customer data.

Correct Answer: B
Option A is incorrect. The Random Cut Forest (RCF) algorithm is better suited to anomaly detection in your data. It is not a choice one would use for a recommendation engine.
Option B is correct. Collaborative filtering is the preferred approach for building an online recommendation engine that leverages customer behavior data.
Option C is incorrect. A Recurrent Neural Network (RNN) is best suited to forecasting scalar (one-dimensional) time series. You don’t have time-series data. You have clickstream data from your website and customer purchasing patterns from in-store and online purchases.
Option D is incorrect. The Neural Topic Model algorithm is best suited for organizing a corpus of documents into topics that contain word groupings based on their statistical distribution. This type of document processing algorithm would not work as a recommendation engine for an online business.
References:
Please see the AWS SageMaker developer guide titled Use Amazon SageMaker Built-in Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html),
The AWS Media blog titled What’s new in recommender systems (https://aws.amazon.com/blogs/media/whats-new-in-recommender-systems/),
The AWS Machine Learning blog titled Building a customized recommender system in Amazon SageMaker (https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagemaker/),
The AWS Database blog titled Using collaborative filtering on Yelp data to build a recommendation system in Amazon Neptune (https://aws.amazon.com/blogs/database/using-collaborative-filtering-on-yelp-data-to-build-a-recommendation-system-in-amazon-neptune/)