A Guide to Recommender Systems

We’re running a special series on recommendation technologies and in this post we look at the different approaches – including a look at how Amazon and Google use recommendations. The Wikipedia entry defines “recommender systems” as “a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages, etc.) that are likely of interest to the user.” That entry goes on to note that recommendations are generally based on an “information item (the content-based approach) or the user’s social environment (the collaborative filtering approach).” We think there’s also a personalization approach, which Google in particular is focused on. We explore some of these concepts below.

In a recent post, Xavier Vespa of the blog HyveUp analyzed 3 different approaches to recommendation engines on the Web. He identified that Pandora used “deep structural analysis of an item” for its recommendations, Strands focused on “intensive social behavior analysis around an item” and Aggregate Knowledge did “structural analysis of an item, paired with behavioral analysis around the item”.

A couple of years ago, Alex Iskold outlined what he saw as the 4 main approaches to recommendations:

Personalized recommendation – recommend things based on the individual’s past behavior
Social recommendation – recommend things based on the past behavior of similar users
Item recommendation – recommend things based on the item itself
A combination of the three approaches above

Amazon: King of Recommendations

In that post, Alex analyzed what Amazon.com – probably the canonical example of recommendations technology on the Web – used to power it’s recommendations. Unsurprisingly, he found that Amazon used all 3 approaches (personalized, social and item). Amazon’s system is very sophisticated, but at heart all of its recommendations “are based on individual behavior, plus either the item itself or behavior of other people on Amazon.” What’s more, the aim of it all is to get you to add more things to your shopping cart.

As Xavier identified, other newer Internet companies have tended to focus on specific methods of recommendation. For Pandora, it is a deeper analysis of the item (using its “gene” theory); Strands has taken a boatload of VC money to try and become the number 1 social recommendations provider on the planet; and Aggregate Knowledge is taking more of a behavioral approach.

Google: Focus on Personalized Recommendations

The most successful Internet company of this era has without a doubt been Google. It too has been using recommendation technologies to improve its core search product. There are two ways that Google does this:

Google customizes your search results “when possible”
based on your location and/or recent search activity;
When you’re signed in to your Google Account, you “may see even more relevant, useful results based on your web history.”

So Google is using both your location and your personal search history to make its search results supposedly stronger. This is very much the ‘personalized recommendation’ approach – and indeed personalization has been a buzzword for Google in recent years. However, the two other types of recommendation are also present in Google’s core search product:

Google’s search algorithm PageRank is basically dependent on social recommendations – i.e. who links to a webpage;
Google also does item recommendations with its “Did you mean” feature.

There are surely other ways recommendations technologies are being deployed in Google search – not to mention the range of other products Google has. Google News, its start page iGoogle, and its ecommerce site Froogle all have recommendation features.

ReadWriteWeb Resources for Recommendation Technologies

Let us know what types of recommendation technologies other companies are using. We also invite you to explore using our custom ReadWriteWeb Resources: