Database Reference
In-Depth Information
suggests a few that the user should value highly. It may not even be necessary to find all
items with the highest expected ratings, but only to find a large subset of those with the
highest ratings.
9.1.2
The Long Tail
Before discussing the principal applications of recommendation systems, let us ponder the
long tail
phenomenon that makes recommendation systems necessary. Physical delivery
systems are characterized by a scarcity of resources. Brick-and-mortar stores have limited
shelf space, and can show the customer only a small fraction of all the choices that exist.
On the other hand, on-line stores can make anything that exists available to the customer.
Thus, a physical bookstore may have several thousand topics on its shelves, but Amazon
offers millions of topics. A physical newspaper can print several dozen articles per day,
while on-line news services offer thousands per day.
Recommendation in the physical world is fairly simple. First, it is not possible to tailor
the store to each individual customer. Thus, the choice of what is made available is gov-
erned only by the aggregate numbers. Typically, a bookstore will display only the topics
that are most popular, and a newspaper will print only the articles it believes the most
people will be interested in. In the first case, sales figures govern the choices, in the second
case, editorial judgement serves.
The distinction between the physical and on-line worlds has been called the
long tail
number of times an item is chosen). The items are ordered on the horizontal axis accord-
ing to their popularity. Physical institutions provide only the most popular items to the left
of the vertical line, while the corresponding online institutions provide the entire range of
items: the tail as well as the popular items.
Figure 9.2
The long tail: physical institutions can only provide what is popular, while on-line institutions can make
everything available
