Exam AWS Certified Machine Learning - Specialty topic 1 question 2 discussion

B see https://en.wikipedia.org/wiki/Collaborative_filtering#Model-based

Content-based filtering relies on similarities between features of items, whereas colloborative-based filtering relies on preferences from other users and how they respond to similar items.

Answer is B : Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR. Collaborative filtering focuses on user behavior and preferences therefore it is perfect for predicting products based on user similarities.

B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR. Collaborative filtering is a technique used to recommend products to users based on their similarity to other users. It is a widely used method for building recommendation engines. Apache Spark ML is a distributed machine learning library that provides scalable implementations of collaborative filtering algorithms. Amazon EMR is a managed cluster platform that provides easy access to Apache Spark and other distributed computing frameworks.

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR. ( TRUE ) Collaborative filtering is a commonly used method for recommendation systems that aims to predict the preferences of a user based on the behavior of similar users. In the case described, the objective is to use users' behavior and product preferences to predict which products they want, making collaborative filtering a good fit. Apache Spark ML is a machine learning library that provides scalable, efficient algorithms for building recommendation systems, while Amazon EMR provides a cloud-based platform for running Spark applications. You can find more detail in https://www.udemy.com/course/aws-certified-machine-learning-specialty-2023

collaborative filtering

'Collaborative filtering is a technique that can filter out items that a user might like on the basis of reactions by similar users.' Source: https://realpython.com/build-recommendation-engine-collaborative-filtering/#what-is-collaborative-filtering

A. NO - content-based filtering looks at similarities with items the user already looked at, not activities of other users B. YES - state of the art C. NO - too generic terms, everything is a model D. NO - combinative filtering does not exist

Collaborative filtering is a technique used by recommendation engines to make predictions about the interests of a user by collecting preferences or taste information from many users. The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B’s opinion on a different issue than that of a randomly chosen person.

B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

I think it should be b

Content-based recommendations rely on product similarity. If a user likes a product, products that are similar to that one will be recommended. Collaborative recommendations are based on user similarity. If you and other users have given similar reviews to a range of products, the model assumes it is likely that other products those other people have liked but that you haven't purchased should be a good recommendation for you.

feature engineering is required, use model based

Answer is "B"

go for B

B is correct https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/