It used to be that if you wanted to buy a book, rent a movie or shop for some music, you had to rely on flesh-and-blood judgment — yours, or that of someone you trusted. You’d go to your local store and look for new stuff, or you might just wander the aisles in what librarians call a stack search, to see if anything jumped out at you. You might check out newspaper reviews or consult your friends; if you were lucky, your local video store employed one of those young cinéastes who could size you up in a glance and suggest something suitable.

New York Times, 10/21/2008

Personalized recommendations are a “key differentiating factor” for my company.

Jeff Bezos, Amazon CEO

I think that once you get beyond 1,000 choices, a recommendation system becomes critical. People have limited cognitive time they want to spend on picking a movie.

Reed Hastings, Netflix CEO

Online recommenders reinforce the blockbuster nature of media.

Hosanagar & Fleder

We had this realization that if we gathered together a really large group of people, like thousands or millions, they could help one another find things, because you can find patterns in what they like. It’s not necessarily the one, single smart critic that is going to find something for you, like, ‘Go see this movie, go listen to this band!

Pattie Maes, M.I.T.

]]> http://hycov.insideabox.gr/2008/12/recommendation-systems-a-success-story/feed/ http://hycov.insideabox.gr/2008/12/recommendations-why-matter/ http://hycov.insideabox.gr/2008/12/recommendations-why-matter/#comments Fri, 05 Dec 2008 15:43:24 +0000 insideabox http://hycov.insideabox.gr/?p=66 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).