Architecting a Solution for Predictive Product Recommendations |
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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/)
To build a recommendation engine for improving sales for an auto parts division based on clickstream data, the recommended approach is to use collaborative filtering. Collaborative filtering is a technique used to make recommendations by finding similarities between users' behavior or preferences. The system recommends items to a user based on the preferences of other users who have similar behavior or preferences.
Out of the four options given, option C, Create a recommendation engine based on a neural collaborative filtering model using TensorFlow and run it on SageMaker, is the most appropriate solution for this scenario.
In a neural collaborative filtering model, a deep learning neural network is used to find similarities between users based on their behavior or preferences. The model is trained using historical data that includes users' interactions with products. The neural network can learn complex patterns in user behavior and recommend items that the user may like based on the behavior of other users who have similar preferences.
TensorFlow is a popular machine learning framework that provides an easy-to-use interface for building and training deep learning models. SageMaker is a managed service offered by Amazon Web Services (AWS) that simplifies the process of building, training, and deploying machine learning models.
Therefore, using a neural collaborative filtering model with TensorFlow and running it on SageMaker will enable the team to build a highly accurate and scalable recommendation engine based on clickstream data for the auto parts division.
