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Answer: Build a collaborative-based filtering model
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. Since the goal is to recommend new products based on purchase behavior and user similarity, a collaborative-based filtering model is the most appropriate choice. 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.
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You are an ML engineer at a global shoe store. You manage the ML models for the company's e-commerce website. The goal is to enhance the customer experience by providing personalized product recommendations. 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?
A
Build a classification model
B
Build a knowledge-based filtering model
C
Build a collaborative-based filtering model
D
Build a regression model using the features as predictors