How the "you might like these products" in webstores is implemented?

Question

Some e-commerce platforms have the suggestion feature where they tell you once you have an item in the basket that "you might like this product as well". Some, like Amazon, rely on the preexisting data on customer behaviour and their feature is called "Customers Who Bought This Item Also Bought" but some seem to suggest by other means.

What are these "other means"? What kind of algorithms do they use in webstores for this capability?

Answer 1

They use data mining, and this particular algorithm you're asking about is called the "nearest neighbor" algorithm.

Here's a link to an article I wrote on the algorithm (as well as many others).

http://www.ibm.com/developerworks/opensource/library/os-weka3/index.html

Answer 2

The process is called Business Intelligence, data will be stored in a data warehouse and the business intelligence process can be used using a product such as SSAS. The process will involve grouping the volumes of data (Who bought what and when) into data cubes. Analysis is performed on these cubes and used to compare your purchases with others who bought the same product, it will then recommend their purchases (Other customers who bought this, also bought this item....Item X). Other various AI algorithms are used to compare patterns across other customer trends such as how they shop, where they click etc. All this data is accumulated and then added to the data cube for analysis.

The data mining algorithms are outlined below, you could look for the Decision Tree Modelling algorithm which is how BI determines trends and patterns (In this case, Recommendations):

http://msdn.microsoft.com/en-us/library/ms175595.aspx