What Is a Recommendation Engine? How It Works and Why Businesses Use It

You open a product page for a tent. Below it sit a sleeping bag, a headlamp and three other tents, and the homepage you return to tomorrow looks a little different from yesterday’s. None of that was arranged by hand.

A recommendation engine picked those products for you, and for most online stores and streaming services it now does more selling than the search box.

What is a recommendation engine?

A recommendation engine is software that predicts which products, videos, articles or offers a particular person is most likely to want, and shows those first. It learns from what people do (views, clicks, purchases, ratings) and from what the items are (category, price, brand, description), and it updates its picks as new behavior comes in.

You meet one every day, even if you never think about it:

• the “Customers also bought” row under a product,
• the “Because you watched” row on a streaming service,
• the “Similar hotels in this area” box on a travel site,
• the next video that starts on its own on YouTube.

Each of these is a short, ranked list built for one person at one moment. The engine behind it decides which few items out of thousands, or millions, deserve that space.

How AI and machine learning drive modern businesses

Recommendation engines are the oldest working example of machine learning in commerce. Amazon launched its item-to-item recommendations in 1998, years before anyone talked about AI strategy. What changed recently is the scale of the money involved and the arrival of conversational AI on top of the classic engines.

A few numbers from the last two years show where things stand:

• Over the 2025 holiday season, AI and agents influenced 20% of global online sales, $262 billion, through personalized recommendations and customer service, according to Salesforce’s analysis of 1.5 billion shoppers. Shoppers arriving from AI-powered search converted nine times more often than those coming from social media.
• Traffic to US retail sites from generative AI tools grew 693.4% year over year in the same season, Adobe Analytics reported, from a still modest base.
• Amazon says its Rufus shopping assistant was used by more than 300 million customers in 2025 and helped deliver nearly $12 billion in incremental annualized sales (fourth-quarter 2025 results).
• On the shopper side, McKinsey’s 2021 survey found that 71% of consumers expect personalized interactions and 76% get frustrated when they don’t get them. The same research puts the typical revenue lift from personalization at 10 to 15%, a consultancy estimate across sectors.

What does that mean for a store that is not Amazon? Mostly that shoppers now expect a store to remember them. A catalog that shows every visitor the same bestsellers feels dated next to one that reacts to the last three clicks.

It also means the bar is lower than it was. You no longer need a research lab to run recommendations; you need clean event data, a sensible model and a way to measure the result.

How different industries use recommendation engines

The mechanics are similar everywhere, but each industry puts recommendations in a different place and asks them to do a different job.

Ecommerce

Online stores use recommendations to raise the value of each order and to help shoppers who arrive without a clear idea. The classic placements are “frequently bought together” on the product page, accessories in the cart, and a personalized homepage. Even a generic electronics store can recommend the next processor in the range to someone comparing two of them.

Travel

Travel sites recommend hotels in the same area and price band, add-ons such as transfers and insurance, and the next trip after the last one. The stakes per booking are high and the purchase is rare, so a good list of alternatives on a sold-out or overpriced hotel keeps the traveler from leaving for a competitor.

Real estate

Property portals show similar homes nearby and send alerts for new listings that match a saved search. A buyer who looks at three two-bedroom apartments in one neighborhood is telling the portal far more than the search filters they typed.

Media, video and social platforms

Here the recommendation engine is the product. YouTube’s VP of Engineering wrote in 2021 that recommendations drive more of the platform’s viewing than channel subscriptions or search, and that the system weighs clicks, watch time, survey answers, shares, likes and dislikes. TikTok explains that a strong signal, such as watching a longer video to the end, counts for more than a weak one. Netflix put the share of hours streamed that start from a recommendation at about 80%.

Advertising

Ad platforms rank which ad to show the same way a store ranks products: the predicted chance of a click or a purchase. Retail media, where a store sells ad space next to its own recommendations, has turned the “Products related to this item” row into a revenue line of its own.

Custom recommendation engine or a ready-made tool?

Most ecommerce platforms ship basic recommendations, and plugins add more. They work well for a standard store: “bestsellers in this category”, “bought together”, “recently viewed”.

A custom engine starts to pay off when your data or your rules do not fit the template. Think of a catalog with millions of items, products that go stale in days (travel offers, classifieds, events), strict business rules such as margins or contract prices, or several sources of behavior that a plugin cannot see.

This is a decision we have made for ourselves. We built Recostream, a recommendation engine for online stores, as our own product. Stores installed it with one line of JavaScript, its models returned a recommendation in 20 to 30 milliseconds under very high event volumes, and stores measured a typical 5 to 10% sales uplift in Google Analytics. GetResponse acquired Recostream in December 2022 (case study).

How do recommendation systems work?

Every recommendation engine, from a plugin to Netflix, runs the same loop: collect behavior, turn it into something a model can read, train the model, serve its picks and learn from the response. The figure shows the five steps; the sections below explain what happens in each.

Step 1: Data collection

The engine needs two kinds of data. Behavioral data records what people do: product views, clicks, add-to-cart events, purchases, returns, ratings, searches, time spent. Item data describes what is being recommended: category, brand, price, stock, description, images.

Implicit signals (a view, a purchase) are far more plentiful than explicit ones (a star rating), so most modern engines learn mainly from implicit behavior. In practice the hardest part is not the volume but consistency: the same product needs the same ID in the web store, the app and the order system.

Step 2: Data processing and cleaning

Raw event streams are messy. Bots and crawlers inflate views, one person appears as three visitors across devices, test orders sit next to real ones, and products get renamed. Cleaning removes the noise, merges sessions, drops items that are no longer sold and joins behavior with the catalog.

Skipping this step is the most common reason a first model disappoints. A model trained on bot traffic learns to recommend what bots click.

Step 3: Feature extraction and engineering

Models do not read events; they read features, numbers that describe a user, an item or the context. Typical examples: how often a user buys from a category, an item’s price band and popularity this week, how many days since the last visit, the device, the time of day.

Text and images can become features too. A language model can turn a product description into an embedding, a list of numbers that places similar products close to each other, which helps with new items that have no sales history yet.

Step 4: Model training

The model learns patterns from past behavior: which items are bought together, which users resemble each other, which features predict a click. Common choices range from matrix factorization and gradient-boosted trees to two-tower neural networks and sequence models that read a user’s history in order.

Large systems split the work in two: a fast retrieval stage narrows millions of items to a few hundred candidates, and a heavier ranking model orders them. Our guide to building a recommendation system covers this architecture step by step.

Step 5: Prediction, recommendation and measurement

When a visitor opens a page, the engine scores candidates for that person and returns a short list, usually in tens of milliseconds, because a slow widget is a widget nobody waits for. Business rules come last: hide out-of-stock items, respect margins, avoid showing the product the person just bought.

Then you measure. An A/B test against the current page (or against no recommendations) is the only reliable way to know whether the engine sells more, and the results feed the next round of training.

How recommendation engines process user data: collaborative, content-based and hybrid

Under the hood, almost every engine combines two basic ideas. One looks at what people do, the other at what items are.

Collaborative filtering

Collaborative filtering recommends what similar people liked. It needs no product descriptions at all, only behavior, which makes it good at surprising, useful suggestions that no category tree would produce.

There are two flavors:

• User-based: find shoppers whose history resembles yours and recommend what they bought.
• Item-based: find items that are often bought or viewed together and recommend the neighbors of what you looked at. Amazon chose this approach because it scales to huge catalogs and updates quickly.

The weakness is the cold start: a new product with no sales, or a new visitor with no history, gives collaborative filtering nothing to work with.

Content-based filtering

Content-based filtering recommends items that resemble what a person already liked, by comparing item features: category, brand, price, material, genre, keywords, or an embedding of the description.

It handles new items well, since a product can be recommended the minute its description exists. The downside is predictability: it keeps offering more of the same and rarely suggests something from a different category.

Hybrid recommendation systems

A hybrid system combines both, and nearly every production engine is one. Content features cover new items and new users; behavior takes over as data builds up. Netflix, Amazon and Spotify all run hybrids, usually with many models feeding one ranking.

For the details of how the two largest examples do it, see our breakdowns of the Amazon recommendation system and the Netflix recommendation algorithm.

Key benefits of a recommendation engine

• Larger orders: cross-sell and upsell at the moment of decision, on the product page and in the cart.
• More conversions: shoppers find a fitting product in fewer clicks, so fewer of them give up.
• Longer sessions and return visits, the main goal for media and content platforms.
• More of the catalog gets sold: Netflix measured that personalization made its effective catalog about four times larger than a popularity list would.
• Less manual merchandising: models refreshed daily replace hand-made “you may also like” lists.
• Better customer insight: what people view together tells you about demand before it shows up in sales.

How big is the effect? McKinsey’s estimate of a 10 to 15% revenue lift from personalization is a reasonable planning range; the stores running Recostream saw 5 to 10% in sales, measured in their own analytics.

Good practices for displaying content recommendations

Where and how you show recommendations matters as much as the model. Five rules hold almost everywhere:

• Place them next to a decision: product page, cart, end of an episode, booking confirmation.
• Say why: “Because you viewed…” earns more trust than an unexplained row.
• Keep lists short, four to eight items on a screen, and never repeat the item on the page.
• Filter out what cannot be bought: sold-out stock, expired offers, things the person already owns.
• Test every change against a control group before rolling it out.

The best placement and the main pitfall differ by industry:

The future of recommendation engines

The ranked list is turning into a conversation. Amazon’s Rufus answers questions and recommends products from them, and Netflix began testing a generative AI search in May 2025 that accepts requests such as “I want something funny and upbeat”. Under the hood, language models now help describe items and read requests, while the classic retrieval and ranking models still decide what to show.

Three other shifts are worth planning for:

• Real time. Netflix announced home-page recommendations that respond to a member’s mood in the moment; shoppers expect the same from a store that has just watched them browse.
• Agents as shoppers. AI agents that compare and buy on a person’s behalf read product data, not banners, so clean catalog data becomes part of recommendation work.
• Transparency rules. In the EU, the Digital Services Act requires very large platforms to offer at least one recommender option that is not based on profiling. Smaller businesses are not covered by that article, but the direction is clear: explain why an item is shown and let people adjust it.

If you are weighing a recommendation engine for your store or platform, our AI integration pilot adds recommendations to an existing system in about four weeks, with an evaluation set, an A/B test and a written decision on whether to roll it out.