Strategic Shift of Amazon’s Recommendations | AI/ML for Product Managers

Every Product Manager should know this

In the early days of e-commerce, the goal was clear:
provide customers with personalized product recommendations.

The initial approach was user-based collaborative filtering.

User to User Collaborative Filtering

This method sought to identify users with similar purchase histories to predict what a given customer might like.

Credits — https://www.baeldung.com/cs/amazon-recommendation-system

The logic was straightforward

If person A and person B have similar tastes, and person A buys product X, then person B might also be interested in product X.

However, this approach quickly hit a wall. There are primarily 3 reasons

1. Computational Bottleneck: Comparing every user’s purchase history with every other user’s became computationally infeasible.
   The sheer volume of Amazon’s customer base made real-time recommendations extremely slow. This was because finding the group of customers whose purchase histories most closely resembled a given…