You are a Machine Learning Specialist on a team that is designing a system to help improve sales for your auto parts division. You have clickstream data gathered from your user`s activity on your product website. Your team has been tasked with using the large amount of clickstream information depicting user behavior and product preferences to build a recommendation engine similar to the Amazon.com feature that recommends products through the tagline of “users who bought this item also considered these items.” Similarly, your team’s task is to predict which products a given user may like based on the similarity between the given user and other users.
How should you and your team architect this solution?

Answer options:

A.Create a recommendation engine based on a neural combinative filtering model using TensorFlow and run it on SageMaker.
B.Create a recommendation engine based on model-based filtering using TensorFlow and run it on SageMaker.
C.Create a recommendation engine based on a neural collaborative filtering model using TensorFlow and run it on SageMaker.
D.Create a recommendation engine based on content-based filtering using TensorFlow and run it on SageMaker.

Correct Answer: C
Option A is incorrect. There is no neural combinative filtering method used in recommendation engine models.
Option B is incorrect. The term model-based filtering is too generic. We are using a model to make our recommendations, but which type of model should we use?
Option C is correct. The famous Amazon.com recommendation engine is built using a neural collaborative filtering method. This method is optimized to find similarities in environments where you have large amounts of user actions that you can analyze.
Option D is incorrect. Content-based filtering relies on similarities between features of items, whereas collaborative-based filtering relies on preferences from other users and how they respond to similar items.
References:
Please see the article titled BUILDING A RECOMMENDATION ENGINE WITH SPARK ML ON AMAZON EMR USING ZEPPELIN (https://noise.getoto.net/2015/11/14/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin-2/),
The Wikipedia article titled Collaborative filtering (https://en.wikipedia.org/wiki/Collaborative_filtering),
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/)