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?

The question requires building a model that recommends products based on a user's purchase behavior and their similarity to other users. This is the definition of collaborative filtering. This method operates on user-item interaction data (e.g., purchases, ratings) to find users with similar tastes. It then recommends items that these similar users have liked but the target user has not yet interacted with. This approach does not require knowledge of item features, relying instead on the "wisdom of the crowd" to make relevant suggestions.

A. A classification model predicts a discrete label (e.g., will buy/won't buy) for a single item, rather than generating a ranked list of recommendations based on user similarity.
B. A knowledge-based filtering model relies on explicit user requirements and domain knowledge (e.g., "show me blue running shoes"), not implicit behavioral data like purchase history.
D. A regression model predicts a continuous value (e.g., a potential rating), which can be a component of a recommender, but it doesn't inherently capture the user-to-user similarity aspect.

1. Google Cloud Documentation: "Choose a model type for recommendations". Google's Recommendations AI documentation describes the "Others you may like" model type, which is a form of collaborative filtering that recommends items frequently purchased or viewed by users who also interacted with the same items as the current user. This directly aligns with the prompt's requirement.

Source: Google Cloud, Recommendations AI Documentation, "Choose a model type", Section: "Others you may like".

2. University Courseware: Ng, A. (2018). CS229 Lecture Notes: Recommender Systems. Stanford University.

Section 1, "The recommender system problem," defines the problem of predicting user preferences. Section 2, "Content-based filtering," is contrasted with collaborative filtering, which is described as leveraging ratings from other users to make recommendations, matching the scenario.

3. Academic Publication: Aggarwal, C. C. (2016). Recommender Systems: The Textbook. Springer.

Chapter 1, "An Introduction to Recommender Systems," Section 1.2, "Basic Models of Recommender Systems," explicitly defines collaborative filtering as a method that "uses the known preferences of a group of users to make recommendations to other users" (p. 5). This is the core principle described in the question.

DOI: https://doi.org/10.1007/978-3-319-29659-3