What is an AI recommendation system?

Software that suggests products, content or actions from behavior and similarity. It learns from clicks, purchases and ratings, then ranks items by how likely each person is to want them.

What are two main types of recommendation systems?

Collaborative filtering suggests what similar people liked, while content-based filtering suggests items like ones you chose before. Hybrid systems combine the two to cover each method's weak spots.

Is the Netflix recommendation system AI?

Yes. Netflix uses recommendation algorithms that learn from viewing behavior, and IBM reports that about 80% of what viewers watch there comes from those suggestions.

Which AI is used in recommendation systems?

Most use machine learning models trained on behavior data. Some add language models to read descriptions or reviews. The right choice depends on your data and goal, not on one best model.

What data does a recommendation system need?

Records of what people viewed, bought or rated, plus details about each item. More history gives better suggestions, and new people or items need time to build a record.

Do shoppers actually use recommendations?

Many do. A published study found shoppers who clicked recommendations were 4.5 times more likely to add items to their cart and complete a purchase. See AI in e-commerce for examples.

AI-Based Recommendation Systems Explained

Definition

AI recommendation system

An AI recommendation system is software that suggests products, content or next actions to a person. It learns from past behavior and from what similar people or items have in common. AI-based recommendation systems power the "you may also like" row in a store.

In short: Software that learns what to suggest next.

How an AI recommendation system works

01
Collect signals
Clicks, purchases, ratings and searches.
02
Build profiles
A picture of each person and each item.
03
Score items
Rank by how likely each is to fit.
04
Show top picks
Display the best few suggestions.
05
Learn from results
New clicks improve the next round.

The system collects signals: clicks, purchases, ratings, searches and time spent. It turns them into a picture of each person and each item.

Then it scores items by how likely the person is to want them and shows the top few. Each new click feeds back in, so suggestions improve with use.

IBM describes this as a recommendation engine and sizes the market at USD 6.88 billion in 2024, expected to triple within five years.

The three main types

Collaborative
Uses the behavior of similar people.
Content-based
Matches items to what you already chose.
Hybrid
Combines both to cover each one's gaps.

Most systems fall into one of three types, and many real ones mix them.

Business uses and the results behind them

Product suggestions
Related items on product and cart pages.
Content suggestions
What to watch, read or listen to next.
Next best action
Suggest the next step on a sales deal.

Stores suggest products, media services suggest what to watch, and sales teams can suggest the next action on a deal.

Salesforce reports that 37% of shoppers who clicked a recommended item on a first visit came back.

Netflix says 80% of what its viewers watch comes from algorithmic suggestions, and McKinsey notes that 35% of Amazon purchases come from product recommendations.

A published study of online shoppers found that a single personalized recommendation click was tied to a 369% rise in average order value.

See AI in e-commerce for the wider picture, and our predictive analytics entry for related forecasting.

Data needs and limits

Works well with

Plenty of past behavior
Clean, honest ratings and reviews
A clear goal to measure

Struggles with

New people with no history
New items with no data
Fake or distorted reviews

Good suggestions need enough history. A new person or a new item has no record, so the system has little to go on. This is called the cold start problem.

Suggestions can also grow narrow, showing more of the same. And they depend on honest data: the FTC has banned fake reviews, including AI-generated ones, which can distort what a system learns.

Because of this, a person should check how suggestions are used and what data feeds them. Our human in the loop entry explains how.

Our data

What AI answers when buyers ask

We asked ChatGPT, Gemini and Perplexity 10 buyer questions about AI for e-commerce brands. They named 130 companies, and 82% appeared in only one of the three.

Criterion	ChatGPT	Gemini	Perplexity
Companies named	45	52	64
Sites cited most	shopify.com (5 of 10)	vellum.ai (4 of 10)	pearllemonai.com and aiagencyradar.com (2 of 10)

Nexum measurement, October 20, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 7 companies appeared in all three. Google showed an AI Overview for 8 of the 10 questions.

FAQ

AI recommendation system questions

Software that suggests products, content or actions from behavior and similarity. It learns from clicks, purchases and ratings, then ranks items by how likely each person is to want them.
Collaborative filtering suggests what similar people liked, while content-based filtering suggests items like ones you chose before. Hybrid systems combine the two to cover each method's weak spots.
Yes. Netflix uses recommendation algorithms that learn from viewing behavior, and IBM reports that about 80% of what viewers watch there comes from those suggestions.
Most use machine learning models trained on behavior data. Some add language models to read descriptions or reviews. The right choice depends on your data and goal, not on one best model.
Records of what people viewed, bought or rated, plus details about each item. More history gives better suggestions, and new people or items need time to build a record.
Many do. A published study found shoppers who clicked recommendations were 4.5 times more likely to add items to their cart and complete a purchase. See AI in e-commerce for examples.

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