You are an ML engineer at a global shoe store.
You manage the ML models for the company's website.
You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users.
What should you do?
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A. B. C. D.C.
https://cloud.google.com/solutions/recommendations-using-machine-learning-on-compute-engineThe most appropriate option for this scenario is C, to build a collaborative-based filtering model.
Collaborative filtering is a type of recommendation system that analyzes the interactions between users and items. The system finds patterns and similarities in user behavior and item attributes to make recommendations to users.
In this case, the recommendation is based on the user's purchase behavior and similarity with other users. Collaborative filtering will compare the user's purchase behavior with that of other users and recommend products that similar users have purchased.
Option A, building a classification model, may not be the best choice as it is used for predicting categorical variables or classes, such as whether a customer will make a purchase or not.
Option B, building a knowledge-based filtering model, would not be suitable as it requires prior knowledge of the user's preferences, and in this case, the goal is to make recommendations based on past purchase behavior and user similarity.
Option D, building a regression model, would not be ideal either, as it is used to predict a continuous value, such as price, and it may not be applicable to this scenario where the goal is to make product recommendations.
Therefore, Collaborative filtering is the most appropriate option for building a model to recommend new products to users based on their purchase behavior and similarity with other users.