What is a recommendation system?

A recommendation engine, also called a recommendation system, is an AI system that predicts which items are most relevant to a specific user and then suggests them. It is the technology behind the “recommended for you” sections on Netflix, Amazon and Spotify. In this article you will read what a recommendation system is exactly, how it works and, honestly, when it is and is not worth it for B2B.

What is a recommendation engine exactly?

According to IBM, a recommendation engine is an AI system that suggests items to a user. It analyses data on behaviour and preferences, recognises patterns and turns them into targeted suggestions. The goal is always the same: get the user to something relevant faster, and so drive more engagement or revenue.

Under the hood, such a system runs on machine learning. It learns from examples which items go together with which behaviour, and sharpens its predictions as more data comes in. The more reliable data, the sharper the recommendations.

How does a recommendation engine work?

There are broadly two classic approaches, plus a combination of them. They differ mainly in the question they ask before recommending something.

Collaborative filtering

The system recommends items based on what similar users did. The logic: people like you found this interesting, so you might too. IBM describes this as grouping by behaviour. That is how Amazon’s “customers who bought this also bought” works. Strong when you have plenty of users with overlapping behaviour. The downside: a new user without a history falls outside the net.

Content-based filtering

The system looks at the characteristics of items you valued earlier and searches for comparable items. According to IBM, this focuses on the preferences of one specific user rather than on a group. Watched a documentary about entrepreneurship? You will get more titles with that same label. This already works with a single user, but quickly stays in the same lane: it rarely surprises you with something new.

Hybrid approach

Most strong systems combine both, to offset the weaknesses of each method. Netflix, for instance, uses the behaviour of similar viewers as well as the characteristics of what you picked yourself. That way you cover one method’s cold start with the strength of the other.

A well-known stumbling block is the “cold start”: for a new user or a new item there is not yet enough data to recommend well. That problem cuts to the core of why recommendation systems are so data-hungry.

The two methods side by side

Characteristic	Collaborative filtering	Content-based filtering
Basis for the recommendation	Behaviour of similar users	Characteristics of items you chose
Needed to start	Many users and interactions	Good item labels and your history
Strongest side	Discovers surprising, new items	Works with just one user
Weakest side	Cold start with new users	Stays in the same lane
B2B suitability	Low: too little volume	Moderate: usable for content suggestions

Does this work in B2B too? An honest answer

Let us be blunt: recommendation engines grew big in environments with many users and many items, such as webshops and streaming services. Those two conditions are usually missing in B2B. You have a narrower audience, fewer “products” and far lower volumes. As a result, a recommendation system rarely gets enough data to become reliable.

If you build an extensive recommendation system for a B2B site with a handful of services and limited traffic, you are investing in complex technology that returns little. That is exactly the kind of choice we advise against. Steer on what counts instead: qualified leads and revenue, not an impressive feature.

Where recommendation logic can genuinely help in B2B is more modest and more targeted:

Recommending content in a knowledge or blog section, to lead visitors deeper into relevant articles.
Suggesting the next step in a long sales cycle, based on what a lead already viewed.
Unlocking internal knowledge, so employees find the right document faster.

Even then the rule holds: start simple. A well-considered, manual internal link structure and clear navigation often do more than an algorithm that has too little data.

A concrete B2B example

Take a software company with an extensive knowledge base and a handful of demo requests per week. The temptation is to build a recommendation system that predicts the “best next service” for each visitor. In practice we see that at those volumes such a thing rarely becomes reliable: the engine bases itself on a few hundred sessions and is essentially guessing. What does work is showing three substantively related articles at the end of every knowledge article, chosen manually or filtered by topic. That is a light form of content-based recommending, it does not cost you a data scientist and it keeps visitors longer in the right content. The gain lies in the simplicity, not in the algorithm.

Common mistakes

A few pitfalls keep coming back when B2B companies get started with recommendation systems.

Picking technology before the problem is defined. Building an engine because it sounds impressive, not because there is a concrete task behind it. Start from the question you want to answer.
Underestimating the data hunger. Without enough users and interactions, recommendations remain guesswork. Calculate upfront whether your traffic reaches those volumes.
Ignoring the cold start. New items or visitors get poor suggestions. Provide a fallback, for example your best-performing or newest content.
No human oversight. An algorithm that quietly shows irrelevant or embarrassing combinations damages trust. Keep an eye on what it suggests.

The link with AI search engines

Interesting detail: the logic behind recommending and the logic behind AI search engines resemble each other. Both try, based on context, to surface what is most relevant to a question. Where a recommendation engine links items to a user, an AI search engine links sources to a question.

That is why the same discipline pays off: make sure your content is so clear and well structured that a system easily recognises it as relevant. That is the core of generative engine optimization, and it is what our GEO agency approach focuses on. If you want to understand how those AI answers come about, also read our explainer on large language models.

Frequently asked questions

What is the difference between collaborative and content-based filtering?

Collaborative filtering recommends based on what similar users did. Content-based filtering looks at the characteristics of items you valued earlier. Many systems combine both in a hybrid approach.

Do I need a lot of data for a recommendation engine?

Yes. Without enough users, items and behaviour, the system finds no reliable patterns. That makes it tricky for B2B with low volumes, where the number of sessions and interactions is often too small to sharpen an algorithm.

Is a recommendation engine worth it for a B2B website?

Usually only to a limited degree. With little traffic and few items, an advanced system returns little. A well-considered manual structure and targeted content suggestions are often more effective and cheaper.

What does this have to do with AI search engines?

The logic is related: both surface what is most relevant based on context. Clear, well-structured content helps a system recognise you as relevant in both cases.

Not sure whether AI features pay off?

Tell us where you stand, and we will say honestly what is and is not worth it for your B2B situation. No technology for technology’s sake, but choices that bring in customers. Book your free intake.

What is a recommendation system? A B2B explainer