Which algorithm is commonly used to suggest products on e-commerce platforms?

The collaborative filtering algorithm is widely used by e-commerce platforms to suggest products because it leverages user behavior and preferences to generate recommendations. This algorithm works by collecting data from multiple users and identifying patterns in their purchasing habits. When a user interacts with a product, collaborative filtering analyzes what similar users have bought and enjoyed, thus suggesting products that the current user may also like based on those similarities.

For example, if User A and User B have similar purchasing histories, and User A buys a new product that User B hasn't tried yet, the system can recommend that new product to User B, reasoning that they are likely to appreciate it based on their shared preferences. This approach enhances the user experience by providing personalized recommendations, increasing the likelihood of additional purchases and customer retention.

In contrast, other algorithms listed do not focus on user behavior or preferences in the same way. Hierarchical algorithms typically deal with structured data and relationships rather than personalized recommendations. Batch processing algorithms handle tasks in large volumes but do not inherently provide suggestions. Random selection algorithms simply make arbitrary choices without consideration of user data, which is generally less effective for personalized product recommendations.