In the digital-first world, you can find a recommendation engine everywhere — from Tinder to Netflix. And you may also use a product recommendation engine in your ecommerce stack to help your customers. But is it annoying or helping your shoppers? We’ll go through some common techniques to help guide you to the most personalized recommendation experience.

Virtual Personal Shopper

If you’ve been in ecommerce for more than a few minutes, you’ve likely heard the stat that more than a third of Amazon’s purchases are a result of product recommendations. Conscious of it or not, a recommendation engine has most likely been your shopping companion every time you have completed a purchase online.

Recommendations can be made at any time:

  • From the moment you log onto a site (ala Netflix)
  • In the search bar (with drop-down suggestions)
  • After you have selected something to purchase (i.e., you might like this, too!)

In fact a whopping 80% of entertainment consumed on Netflix is because of its content recommendation engine. That shouldn’t be surprising, says Louis Têtu, CEO of Coveo. “Netflix wants you watching — not searching.”

What Makes a Recommendation Engine So Powerful?

Circling back to the top: what is a recommendation engine? And what makes it so powerful?

For example, Amazon’s product recommendation system predicts what item User A might be interested in and guides that user to the most relevant content — be it products, services, or information. The beauty of this is that the user is exposed to content they might not have known about — but thanks to artificial intelligence may find the perfect match regardless.

With a goal of delivering the best user experience, be it getting users to swipe right selling romance or stick-on-tile, a recommendation engine powered by artificial intelligence uses principles like data mining, deep learning, etc. to analyze big data but in a narrow set of approaches.

Predictions Based on Ratings

Think of a recommendation system like a ratings engine. The higher the rating the more likely it will be “liked” by a consumer. High ratings signal highly relevant product recommendations. And while ratings can be explicit or implied often they are binary:

Purchase = 1     No purchase = 0

To generate meaningful, personalized product recommendations, the recommendation system must have a fair amount of data to determine a recommendation strategy. That data is then filtered accordingly so that the user is exposed to the most relevant product he/she is most likely to buy. 

So what kinds of data will predict behaviors? Previous buyers’ histories is one of the most common types of data. Generating relevant product recommendations based on similarity of historical searches is a popular recommendation strategy in ecommerce. On Tinder, by the way, the more a user swipes right, the more power they have as a data point for other users on the app.

Collaborative Filtering: Unicorn Baby Items for Days

Many recommender systems, such as Amazon’s and Netflix’s, work by using artificial intelligence models to aggregate the behavior histories of all of their shoppers. They analyze that data so that the system can predict behavior of what similar users have done in the past.

In marketing this is called persona-based personalization. Whereas in data science and computer science, researchers call this collaborative filtering.

Advantages to CollaborativeFiltering

  • Gives a great starting point for most visitors
  • Provides serendipity — a way to explore and find what you weren’t looking for

Disadvantages to CollaborativeFiltering

  • Cannot handle new items in the catalog (since no one else has looked at it)
  • Does not include variants or accessories based on similarity for queried items 
  • Reliant on big data; requires a lot of user preference and behavior data
  • It also may turn out to be just plain wrong

Collaborative filtering/persona-based merchandising works… kind of. It can also introduce a lot of noise. I once bought a unicorn blanket for a friend’s new baby from an online retailer. Now, I am constantly deluged with unicorn-themed baby items — across all of my devices.  Luckily, there are other ways to provide a user with an accurate recommendation that has a positive impact on customer experience. 

Unicorn items

Content Based Filtering: Reduce Annoyance of Unwanted Products

The best recommendation engines avoid annoying customers in this way through content based filtering. This real-time analysis of a shopper’s behavior offers the most precise and personalized way to handle relevant recommendations.

Prior user history is considered but is weighted much lower than what a shopper is doing while in the current shopping session. With a content-based recommender engine, everything the shopper is looking at counts, including product attributes-such as size, description, color, and price point. All of this is funneled by the algorithm into compiling similar products, more likely to be added to the cart and purchased.

An effective content-based system is like an astute personal shopper. The customer is happy because it is easier to make smarter choices, faster. And the retailer is happy because the recommendation has driven more sales by pointing the customer to a similar item they might be have otherwise missed! 

Advantages to Content-Based Filtering

  • Collaborative approach that can provide truly personalized recommendations
  • Does not rely on aggregate user behavior data
  • Reduces cold start problems with new products

Disadvantages to Content-Based Filtering

  • Requires a good amount of product data (brand, variants, colors, size, descriptions, accessories, etc.)
  • Can limit recall if narrowly tuned to user search

Machine Learning Recommendations

Traditionally, creating a recommendations engine has been a highly manual process involving intensive analysis of shoppers’ behavior that then gets hardcoded into an algorithm. Think of it as “if-then” situations from school tests. The thinking goes that, if shoppers buy baby blankets, then they may want to buy another similar product.

The algorithm or set of rules may work some of the time (although not all of the time, per my unicorn baby example from above, or this poor person’s struggle with toilet seats ). However, it’s not unusual for legacy systems to have literally thousands of algorithms. And you likely won’t know when they conflict.

The best recommendation engine then is based on advanced machine learning algorithms , says Simon Langevin, director of product management for Coveo.

Hybrid Recommendation Systems: Collaborative and Content-based Filtering

Recommendation engines don’t just feed on data about user behavior, though. They also savor all of the rich information in product catalogs and descriptions. If you are looking for women’s golf slacks, size 4, the attributes of the slacks you have selected are identified.

In this case,

Womens, size 4, golf, slacks

If you then were to search for gloves, the prior attributes would dynamically order the autosuggest to:

Sporting gloves → golf

[Note: You can see dynamic autosuggests in detail by checking out Jacopo’s blog on how to grow a product tree.]
See dynamic autosuggests in detail
How to Grow a (Product) Tree: Building Personalized Category Suggestions with Ludwig

It’s only recently that machine learning has enabled recommendation engines that can suss out these more subtle relationships. With natural language processing and understanding, it’s possible to “vectorize” the catalog .

Understanding the product catalog is ideal when launching a new product, line of business, website or app. If, for example, your retailing website has a new, cutting-edge TV that’s debuting, you want it to show up in recommendations before users have bought or even browsed it. This is a great solution to the cold-start problem.

Content based filtering on product catalogs are so useful, in fact, that recommendation engine algorithms can work solely on catalog information when there’s an absence of user behavior.

Of course we don’t advise that. A hybrid filtering approach is best, because all that rich buyer history from all touch points should never go to waste!

Want to start creating more personalized recommendations? Learn how Coveo delivers relevance in commerce.

Ecommerce Search & Discovery
More Relevance, More Buying with AI-Powered Personalization

Dig Deeper

Learn more about what makes a great recommendation engine in this 6-minute read.

Looking for a recommendation engine? Make it easy and check out our 6 Most Popular Recommenders to Entice Shoppers.

Ready to test out a collaborative filtering-based recommendation platform like Coveo? Try out a demo today.