10 Things You Need to Know About Collaborative Filtering

Mar 16, 2023 11:00:00 AM
10 min read

As online retailers strive to deliver personalized shopping experiences to their customers, a certain tool is becoming more and more popular. Many e-commerce companies are adopting recommendation systems—more specifically, collaborative filtering—to provide tailored product recommendations and boost their bottom line.

Yet many people still don’t know much about this increasingly commonplace tool.

PersonalizationTo put it simply, collaborative filtering creates a prediction based on a shopper’s previous behaviors. Think of it like your best friend recommending the latest action movie because they know you love a good chase scene. The only difference is that recommender systems like collaborative filtering need to get to know you first in order to be successful.

Since 75% of customers are more likely to buy based on personalized recommendations, it’s important to understand what collaborative filtering is and why it’s so effective. In this blog, we’ll explore 10 things every e-commerce business should know about collaborative filtering, including what it is, how it’s used, and its pros and cons. Let’s dive in!

What is collaborative filtering?

Person with blindfold shruggingCollaborative filtering is a type of recommendation system that identifies the preferences or interests of users based on data collected from other users. By analyzing patterns in the behavior of a large group of users, the algorithm can then use that information to make personalized recommendations to individual users, even if it hasn’t interacted directly with them yet.

Some of the data it collects includes the types of products a user has searched for, viewed, clicked on, added to their cart, or purchased before. The purpose? To provide shoppers with product recommendations they’ll actually like, increasing the chances they’ll buy them.

How does collaborative filtering work?

Collaborative filtering is calculated based on a customer’s historical choices. An embedded vector is used to define each shopper and item, then place both in a similar embedding location. Keeping track of other purchasers’ reactions, it then suggests the products that are most liked by those users. This increases the chances of displaying similar products to those who have enjoyed a product previously.

granify_hero_illustrationUltimately, collaborative filtering focuses on the connection between a product and an online store’s shoppers. When done correctly, this technique can boost a site’s average order value and leave customers feeling like they received a personalized experience.

What steps are involved?

Collaborative filtering involves three main steps:

Step 1: Collect the data.

The collaborative filtering system collects customer behavior, such as searches, purchases, and ratings, based on site interactions.

Cartoon calculator

Step 2: Calculate the similarities.

The system then analyzes parallels between different users, based on their preferences and behaviors.

Step 3: Generate product recommendations.

Finally, the system produces recommendations that will likely appeal to the shopper.

What are the types of collaborative filtering?

There are two primary types of collaborative filtering:

1. User-Based

This approach measures the likeness between target users and other users. By examining the past behaviors and preferences of a customer and comparing them with other similar customers, the system is able to identify items they might enjoy as well.

Open cartoon box

2. Item-Based

With item-based collaborative filtering, recommendations are based on the similarity between various products. By identifying items a customer has already interacted with and calculating their similarity, the system is able to suggest other items comparable to what that shopper already likes.

What are the main approaches of collaborative filtering?


Person with computer chip brainAlso known as the neighborhood approach, the key purpose of the memory-based method is to leverage the behavior of others to determine what a user might enjoy. This is achieved by defining the level of similarity between users or items and finding identical ratings to recommend products that haven’t been seen yet. These statistical techniques are applied to the entire dataset to calculate the predictions based on user comparison.

Let’s say Customer E is browsing through patio furniture on an e-commerce site. The system identifies that they share similar interests and shopping behaviors to Customers A, B, C, and D. Using collaborative filtering, the engine is able to recommend outdoor sectionals to Customer E that these other customers were interested in.

Customer A B C D E
Outdoor Dining    
Outdoor Sectionals ???
Patio Umbrellas      

   ✓ = item has been purchased


Mixed bar graph and line graphThis method uses machine learning algorithms to forecast and calculate how a customer rates each product. The algorithms are divided into different subsets, such as Matrix factorization, or clustering. The matrix factorization technique reduces the rating matrix dimension and identifies prospective features under the rating matrix, which then provides several recommendations. A clustering algorithm is typically used to identify the nearest embedding consistent with a similar matrix.

In model-based collaborative filtering, a numerical model is used to identify underlying patterns in the user-item matrix and generate recommendations. Unlike memory-based approaches, this filtering can handle non-linear relationships between users and items as well. The downside? Model-based collaborative filtering can be pretty expensive and requires a lot more resources than memory-based approaches. Fortunately, companies like Granify are able to effectively leverage data from the billions of shoppers we’ve optimized, saving you both time and money!

How are similarities measured?

Three methods are most commonly used:

Cosine Similarity

In collaborative filtering, each user or item is characterized as a vector, with each component corresponding to a specific attribute. Cosine similarity measures the similarity between two vectors. The higher the score, the more alike the two vectors are. This information is then used to determine which products should be recommended to each user, based on their similarities with other site users.

Euclidean Distance

Triangle rulerEuclidean distance is used to measure the similarity between two items or users and is computed by taking the square root of the sum of squared differences between corresponding ratings for each user or item. This calculation can then determine which items or users are most comparable, which can then be used to make hyper relevant product recommendations.

Pearson Correlation

The Pearson correlation is used to identify similar items by computing the similarity between two user’s ratings for different items. The higher the correlation coefficient, the more similar interests. The lower the value, the greater the differences between user preferences. Retailers can use this information to recommend content for each customer, based on what other similar customers have liked in the past.

When can collaborative filtering be used?

The intention of collaborative filtering is to recommend products to e-commerce shoppers visiting a site that entices them to take action. Therefore it should be used any time a customer is looking to purchase something. This can include:

  • Helping shoppers find the products they are looking for more quickly through personalized search results.
  • Upselling or cross-selling by suggesting complementary or more luxurious items.
  • Targeting specific customers with personalized offers or promotions.

What are the pros and cons of collaborative filtering?

The Pros

It enhances personalization.

Its unique algorithms allow e-commerce retailers to provide personalized recommendations and search results, which 76% of customers now expect when shopping online.

It improves accuracy.Option 2B-1

Collaborative filtering yields more precise outcomes than other methods because it is better able to capture user preferences.

Shoppers discover more products.

By using other customers’ ratings to recommend products that haven’t yet been seen, shoppers are introduced to more possibilities to add to their carts.

It doesn’t require product details.

Collaborative filtering doesn’t require a lot of input to generate recommendations. If information about a product isn’t available, it can still be rated easily without causing any delays in the user buying it.

It increases revenue.

Many retail companies generate a high volume of sales by implementing these systems on their websites, resulting in more upsells, cross-sells, and overall conversions.

It highlights long-tail items.

By recommending niche products that are unique or hard to find, customers get access to items they otherwise may have not known about.

It provides a better user experience.

By putting in the effort to understand your customers’ needs through collaborative filtering, you’ll be able to create individualized shopping experiences for each customer that increase engagement and improve brand loyalty.

The Cons

Data can be too limited.

Confused person shruggingCollaborative filtering requires a large amount of user-item interaction data. Without this, recommendations won’t be as accurate.

It can lead to cold start.

This problem arises when a new user or item that has not yet accumulated enough data to make accurate recommendations is introduced to the system, resulting in inaccurate or irrelevant recommendations.

Scalability can be challenging.

As more users are added and the amount of data multiplies, collaborative algorithms may begin to wane in performance due to the increase in data volume.

There may be a lack of diversity.

Because this technique usually recommends products similar to the ones a shopper has already viewed or purchased, it could result in a limited pool of recommendations.

Data quality could be put into question.

Collaborative filtering depends on users’ actions to generate recommendations. If the information used is incorrect or incomplete, the subsequent recommendations can be unreliable, and ultimately ineffective.

What are some examples of collaborative filtering in e-commerce?


Amazon is the most obvious example of a company that incorporates this technique—opting for item-based collaborative filtering—to produce high-quality recommendations. Results have proven to be extremely successful, with profits making the brand the most popular retailer online.

Amazon collaborative filtering


Known for its colossal collection of shows, Netflix uses collaborative filtering to narrow down options by recommending movies and TV shows based on a user’s past viewing history, as well as the viewing habits of similar users.

Netflix collaborative filtering

Apple Music

Apple Music is known for suggesting artists, playlists, and albums to its
subscribers. This is all determined by collaborative filtering, which analyzes what music a user has listened to in the past, as well as similar listening habits from other subscribers.

Apple Music collaborative filtering

What five factors should be considered when outsourcing collaborative filtering?

Integration and Implementation

Try to find a recommender system (like Granify!) that is compatible with your existing e-commerce platform or offers seamless integration. The longer it takes to set up, the more $$$ it will end up costing you.

Data Security

privacy1Collaborative filtering requires access to sensitive customer data. To protect this information, ensure your provider adheres to relevant privacy regulations and has appropriate security measures in place. Look for companies that are GDPR Compliant, part of the Privacy Shield Framework, or AICPA SOC Service Organizations. (Granify is SOC 2® certified.)

Maintenance and Analysis

It’s important to know if your e-commerce strategy is working, so make sure your provider has an analysis or reporting tool to regularly monitor your results. You’ll also want to find out any maintenance requirements, which can add up if your system isn’t fully automated.

Sufficient Scalability

As your customer base and products grow and change, you’ll need a system that is able to adapt to your shifting business needs. An efficient provider will be able to both monitor and optimize these techniques as your company changes.


When breaking down the cost of a system, consider its benefits to your budget. Then, compare costs from different providers who meet your criteria.  

It’s a new wave of customer segmentation.

As the retail world continues to transform, so do the expectations around product recommendations. With collaborative filtering, e-commerce stores can tap into user data to produce tailored recommendations that reflect each customer’s specific preferences. By better understanding what your customers want, you’ll be able to provide more personalized experiences that will drive loyalty and increase your shoppers’ satisfaction. Just make sure to choose an approach that aligns with your e-commerce requirements (and goals), and you’ll be on your way to delivering a more dynamic customer experience that delivers on both personalization and revenue.

Happy woman shopperGive your customers what they want.

Granify's AI-driven machine learning technology provides valuable real-time insights that result in personalized product recommendations your customers want to receive (when they want to receive them). Talk to us today to find out how you can optimize your e-commerce site for maximum conversions and customer satisfaction.

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