I've always been curious as to how these systems work. For example, how do netflix or Amazon determine what recommendations to make based on past purchases and/or ratings? Are there any algorithms to read up on?
Just so there's no misperceptions here, there's no practical reason for me asking. I'm just asking out of sheer curiosity.
(Also, if there's an existing question on this topic, point me to it. "Recommendations system" is a difficult term to search for.)
The O'Reilly book "Programming Collective Intelligence" has a nice chapter showing how it works. Very readable.
The code examples are all written in Python, but that's not a big problem.
The Netflix algorithm for its recommendation system is actually a competitive endeavor in which programmers continue to compete to make gains in the accuracy of the system.
But in the most basic terms, a recommendation system would examine the choices of users who closely match another user's demographic/interest information.
So if you are a white male, 25 years old, from New York City, the recommendation system might try and bring you products purchased by other white males in the northeast United States in the age range of 21-30.
Edit: It should also be noted that the more information you have about your users, the more closely you can refine your algorithms to match what other people are doing to what may interest the user in question.
This is a classification problem - that is, the classification of users into groups of users who are likely to be interested in certain items.
Once classified into such a group, it is easy to examine the purchases/likes of other users in that group and recommend them.
Therefore, Bayesian Classification and neural networks (multilayer perceptrons, radial basis functions, support vector machines) are worth reading up on.
This is such a commercially important application that Netflix introduced a $1 million prize for improving their recommendations by 10%.
After a couple of years people are getting close (I think they're up around 9% now) but it's hard for many, many reasons. Probably the biggest factor or the biggest initial improvement in the Netflix Prize was the use of a statistical technique called singular value decomposition.
I highly recommend you read If You Liked This, You’re Sure to Love That for an in-depth discussion of the Netflix Prize in particular and recommendation systems in general.
Basically though the principle of Amazon and so on is the same: they look for patterns. If someone bought the Star Wars Trilogy well there's a better than even chance they like Buffy the Vampire Slayer more than the average customer (purely made up example).
One technique is to group users into clusters and recommend products from other users in the same cluster.
At it's most basic, most recommendation systems work by saying one of two things.
User-based recommendations:
If User A likes Items 1,2,3,4, and 5,
And User B likes Items 1,2,3, and 4
Then User B is quite likely to also like Item 5
Item-based recommendations:
If Users who purchase item 1 are also disproportionately likely to purchase item 2
And User A purchased item 1
Then User A will probably be interested in item 2
And here's a brain dump of algorithms you ought to know:
- Set similarity (Jaccard index & Tanimoto coefficient)
- n-Dimensional Euclidean distance
- k-means algorithm
- Support Vector Machines
You'll find more information about this subject if you look for:
GroupLens Research at the University of Minnesota studies recommender systems and generously shares their research and datasets.
Their research expands a bit each year and now considers specifics like online communities, social collaborative filtering, and the UI challenges in presenting complex data.