Amazon's 'Customers Also Bought' — The Recommendation Algorithm That Changed Retail
Amazon's 2001 patent describes item-to-item collaborative filtering — the 'customers who bought this also bought' algorithm that generates personalized recommendations in real time, responsible for an estimated 35% of Amazon's revenue.
Original patent title: “Collaborative recommendations using item-to-item similarity mappings”
What this patent covers
The actual claim
This patent describes a method for computing item-to-item similarity from purchase history, then using that similarity table to generate real-time recommendations. For each product in the catalog, the algorithm computes a list of other products most commonly purchased by the same customers. This 'item similarity table' is precomputed offline. When a user visits a page, the system looks up the similarity scores for that item and returns the most similar items as recommendations — in milliseconds, because the heavy computation has already been done. This is fundamentally different from user-to-user collaborative filtering (which compares users to similar users), and dramatically faster for large catalogs because the item table can be precomputed and cached.
What this patent does NOT cover
The boundaries
- Content-based filtering — using product attributes (genre, price, brand) rather than purchase history to make recommendations
- Deep learning recommendation systems — modern neural network approaches that replaced simple collaborative filtering
- Real-time streaming updates — the original algorithm used batch-computed similarity tables; real-time ML models came later
- Sponsored product recommendations — paid placement in recommendation slots is a separate advertising product
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
In the late 1990s, collaborative filtering at Amazon required comparing each user to every other user — with millions of users, this was computationally prohibitive in real time. Greg Linden, a researcher at Amazon, proposed a solution: instead of comparing users to users, compare items to items. Items don't change their purchase history as rapidly as users change their preferences, so an item-to-item similarity table can be computed once daily (or weekly) and cached. Real-time recommendations then require only a single table lookup — instant, regardless of catalog size. The result: recommendations that feel personalized but can be served to millions of users simultaneously with minimal computation. Amazon's product recommendation engine, powered by this algorithm, generates an estimated 35% of total Amazon revenue.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Amazon's 'Customers who bought this item also bought' and 'Frequently bought together' widgets are direct implementations of item-to-item collaborative filtering
Netflix estimated that 75% of content watched comes from recommendations — their early recommendation system used collaborative filtering before transitioning to deep learning
Spotify's 'Discover Weekly' and YouTube's 'Up next' both descend from the collaborative filtering framework, though with modern deep learning layers added
Why it matters
The bigger picture
Recommendation systems fundamentally changed the economics of discovery online. Before them, users had to search — they needed to know what they wanted. Recommendations create demand by surfacing products users didn't know they wanted. Amazon's 35% revenue attribution to recommendations is one of the most-cited statistics in e-commerce. The algorithm also enabled the 'long tail' economics Chris Anderson described — recommendation systems surface niche products to the subset of users who want them, making it economically viable to stock thousands of low-volume items that would be impossible to surface through search or traditional retail.
Filed
September 18, 1998
Granted
July 24, 2001
Claim 1 — Plain English
What this patent covers
This patent describes a method for computing item-to-item similarity from purchase history, then using that similarity table to generate real-time recommendations. For each product in the catalog, the algorithm computes a list of other products most commonly purchased by the same customers. This 'item similarity table' is precomputed offline. When a user visits a page, the system looks up the similarity scores for that item and returns the most similar items as recommendations — in milliseconds, because the heavy computation has already been done. This is fundamentally different from user-to-user collaborative filtering (which compares users to similar users), and dramatically faster for large catalogs because the item table can be precomputed and cached.
The clever bit
In the late 1990s, collaborative filtering at Amazon required comparing each user to every other user — with millions of users, this was computationally prohibitive in real time. Greg Linden, a researcher at Amazon, proposed a solution: instead of comparing users to users, compare items to items. Items don't change their purchase history as rapidly as users change their preferences, so an item-to-item similarity table can be computed once daily (or weekly) and cached. Real-time recommendations then require only a single table lookup — instant, regardless of catalog size. The result: recommendations that feel personalized but can be served to millions of users simultaneously with minimal computation. Amazon's product recommendation engine, powered by this algorithm, generates an estimated 35% of total Amazon revenue.
What it does not cover
- Content-based filtering — using product attributes (genre, price, brand) rather than purchase history to make recommendations
- Deep learning recommendation systems — modern neural network approaches that replaced simple collaborative filtering
- Real-time streaming updates — the original algorithm used batch-computed similarity tables; real-time ML models came later
- Sponsored product recommendations — paid placement in recommendation slots is a separate advertising product
Patent Journey
From filing to expiry
Patent Filed
1998
Patent Granted
2001 · 3yr after filing
Highly Cited
899 patents cite this
Patent Expired
2018
PatentBrief Score
Impact Score
High impact
Citation count
40/40
Highly cited
Claim breadth
20/20
Very broad protection
Recency
0/20
Older than 20 years
Assignee scale
20/20
Major technology company
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
The original legal language
Original claims
54 claims as filed with the patent office.
Glossary
Key terms defined
- long tail
- The phenomenon where online retailers can profitably stock thousands of niche products by using recommendations to surface them to interested users
- collaborative filtering
- A recommendation technique based on the behavior of similar users or items — 'what do people who bought/watched X also buy/watch?'
- item-to-item similarity
- A precomputed table recording which items are most frequently purchased together — Amazon's specific innovation was doing this at item level rather than user level
Citations
Patent lineage
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