Getting Started with Recommender Systems

Have you ever wondered how those personalized recommendations are developed that we see across the Internet? Maybe you have a set of product and product attribute data or a set of product rating data from your users, and you would like to turn this into data into personalized recommendations for your visitors?

Recommender Systems seek to provide recommendations to a user based on inferred knowledge of their preferences. These recommendation engines are quite popular in websites and mobile applications, whether on shopping sites such as Amazon, or content sites such as Pandora or Netflix. The fundamental premise of Recommender Systems is to develop relationships and uncover dependencies between users and items, using this information to predict what a particular user would prefer.

There are two broad approaches to recommender systems:

Content-Based systems use information and attributes from the items themselves to predict user ratings. For example, Pandora is known to use hundreds of descriptors of each song, such as the artist, rhythm, mood, and tempo, to provide individual song recommendations, as part of their overall recommendation algorithm.

Related posts:

One of the main reasons we should be concerned about the decline of union memberships is because there has been a consistent trend between union membership and income inequality. It’s a little known…

A Penetration Test is a test performed by ethical hackers also known as white hats attempting to breach your organization’s security. The purpose of this Testing method is to identify exploitable…