8 Powerful Types of Recommendations for Ecommerce Stores

Now that you know how recommendation systems work, let’s look at the main types you can use—and when to use them.

1. Collaborative filtering systems

Collaborative filtering is one of the most popular and effective recommendation strategies. It works on the principle that people who behaved similarly in the past will behave similarly in the future.

Instead of focusing on the products themselves, collaborative filtering zeroes in on user interactions. It identifies patterns in behavior—like purchases, likes, or clicks—and then suggests products based on what other users with similar actions have enjoyed.

For example, If User A and User B both bought wireless earbuds and User A also bought a phone case, the system might suggest the same phone case to User B.

There are two main approaches:

• User-based collaborative filtering: Finds users similar to the current user and generates recommendations based on what they liked.
• Item-based filtering: Finds items similar to what the user interacted with (or frequently purchased) and recommends those.

The downside? Collaborative filtering requires a lot of user data to be truly effective. It can also struggle with new users or products, a challenge known as the “cold start problem.”

2. Content-based filtering systems

Content-based filtering focuses entirely on the product features and user preferences. It doesn’t compare users with each other—it looks at what an individual user has engaged with and finds similar products based on attributes.

If someone buys a pair of black leather boots, the system will recommend other leather footwear or similar fashion items that share those characteristics.

This model is great for stores with a detailed product catalog. The more descriptive your product metadata—like category, brand, size, color, material—the better content-based filtering performs.

It also avoids the cold start issue since it doesn’t rely on crowd behavior.

3. Hybrid recommendation systems

Hybrid systems combine collaborative and content-based filtering to offer the best of both worlds. They can recommend a wider variety of products with more accuracy and less bias.

Netflix is a classic example. It recommends shows based on what you’ve watched (content-based) and what others with similar tastes are watching (collaborative). In ecommerce, a hybrid model could suggest items that are similar to your browsing history but also trending among people like you.

This type of system is ideal for larger stores or platforms that want high-precision personalization. While it’s more complex to implement, the payoff is substantial in terms of engagement and conversion rates.