Prefence predicting - long-tail of curve

Recommendation engines deliver items that more closely meets customer’s wants. Traditionally, web pages display “other users like.” This is similar to offering the movies Transformers and The Hangover. These items are the popular items and lie in the fat of the bell curve. They are a small percentage. This ignores the lesser known items in the long-tail that will be enjoyed by the customer. Netflix delivers movies that are lesser known but that receive high ratings by the user. It uncovers the unknown. Otherwise, only a handful of popular movies would be watched and the customer would end his/her subscription.

Online stores increase the popularity of popular items by offering "other users like" to the buyer. A user becomes aware of the item and is more likely to purchase the item thereby making the item more popular. The effect is a self-perpetuating popularity list. New items that have not been purchased will not make the "other users like" list regardless of matching a user's preferences.

For the online store, items that meet a customer's wants are displayed. Given the limited space available for user browsing, hitting a customer's wants is more valuable.