Posted on December 5, 2008 - by insideabox

Recommendations? Why bother?

In everyday life, people often make choices without sufficient personal experience of the alternatives. They rely on recommendations from other people either by word of mouth, movie and book reviews printed in newspapers, or general surveys. The advent of shopping over the Web completely upended this cultural and economic ecosystem. There are no clever clerks to ask for advice and online stores like Amazon or iTunes can stock millions of titles, making a stack search essentially impossible. This creates the classic problem of choice:

How do you decide among an effectively infinite number of options?

Or from a businessman’s point of view:

Did you ever wonder how you can get that visitor/user/customer to realize that you offer something of value to him or her?

Recommender Systems are a conceptual answer. Plus, they are here to stay.

Recommender Systems attempt to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user/client. Typically, a recommender system compares the user’s profile to some reference characteristics. These characteristics may be from the user’s social environment (collaborative filtering) or the item’s metadata.

Recommender Systems can make automatic predictions about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of this approach is that those who agreed in the past tend to agree again in the future. For example, a recommendation system for music tastes could make predictions about which music a user should like given a partial list of that user’s tastes (likes or dislikes).