Exam Professional Machine Learning Engineer topic 1 question 24 discussion

C. Collaborative filtering is about user similarity and product recommendations. Other models won't work

C. Collaborative filtering is a foundational model for building a recommendation system as the input dataset is simple and the embeddings are learned for you. Matrix factorization is simply the model that applies to the collaborative filtering.

C) Collaborative filtering model

Chat gPT: Collaborative filtering models are specifically designed for recommendation systems. They work by analyzing the interactions and behaviors of users and items, then making predictions about what users will like based on similarities with other users. In this case, since you're looking at purchase behavior and user similarities, a collaborative filtering approach is well-suited to identify and recommend products that users with similar behaviors have liked or purchased. Classification models (Option A) and regression models (Option D) are generally used for different types of predictive modeling tasks, not specifically for recommendations. A knowledge-based filtering model (Option B), while useful in recommendation systems, relies more on explicit knowledge about users and items, rather than on user interaction patterns and similarities, which seems to be the focus in this scenario.

C. Collaborative filtering is apt amongst the answers

Went with C

ans: C

C https://cloud.google.com/blog/topics/developers-practitioners/looking-build-recommendation-system-google-cloud-leverage-following-guidelines-identify-right-solution-you-part-i

This is a textbook application of collaborative filtering, C is the correct answer

Correct answer is "C"

https://developers.google.com/machine-learning/recommendation/collaborative/basics

Definitely C

Community vote

should be C

C - https://cloud.google.com/architecture/recommendations-using-machine-learning-on-compute-engine#filtering_the_data