Question

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?

Answer 1

To build a model that recommends new products based on purchase behavior and similarity with other users, you can use collaborative filtering techniques. There are two main types: user-based and item-based collaborative filtering.

Step-by-step explanation:

To build a model that recommends new products to users based on their purchase behavior and similarity with other users, you can use collaborative filtering techniques. Collaborative filtering is a commonly used approach in recommender systems that leverages the behavior of similar users to make personalized recommendations. There are two main types of collaborative filtering: user-based and item-based.

In user-based collaborative filtering, you find users with similar purchase behavior and recommend products that those similar users have bought but the current user hasn't. For example, if User A and User B have bought a similar set of products, and User A has bought a new product that User B hasn't, you can recommend that new product to User B.

In item-based collaborative filtering, you find products that are similar to the ones the user has already bought and recommend those similar products. For example, if the current user has bought a pair of running shoes, you can recommend other running shoes that are similar in terms of features, brand, or style.