Recommendation Systems - Mining of Massive Datasets - page 295

Into Thin Air and Touching the Void

An extreme example of how the long tail, together with a well designed recommendation system can influence events
is the story told by Chris Anderson about a topic called Touching the Void . This mountain-climbing topic was not a
big seller in its day, but many years after it was published, another topic on the same topic, called Into Thin Air was
published. Amazon's recommendation system noticed a few people who bought both topics, and started recommend-
ing Touching the Void to people who bought, or were considering, Into Thin Air . Had there been no on-line bookseller,
Touching the Void might never have been seen by potential buyers, but in the on-line world, Touching the Void even-
tually became very popular in its own right, in fact, more so than Into Thin Air .

The long-tail phenomenon forces on-line institutions to recommend items to individual
users. It is not possible to present all available items to the user, the way physical institu-
tions can. Niether can we expect users to have heard of each of the items they might like.

9.1.3 Applications of Recommendation Systems

We have mentioned several important applications of recommendation systems, but here
we shall consolidate the list in a single place.

(1) Product Recommendations : Perhaps the most important use of recommendation sys-
tems is at on-line retailers. We have noted how Amazon or similar on-line vendors
strive to present each returning user with some suggestions of products that they might
like to buy. These suggestions are not random, but are based on the purchasing de-
cisions made by similar customers or on other techniques we shall discuss in this
chapter.
(2) Movie Recommendations : Netflix offers its customers recommendations of movies
they might like. These recommendations are based on ratings provided by users, much
like the ratings suggested in the example utility matrix of Fig. 9.1 . The importance of
predicting ratings accurately is so high, that Netflix offered a prize of one million dol-
lars for the first algorithm that could beat its own recommendation system by 10%. 1
The prize was finally won in 2009, by a team of researchers called “Bellkor's Prag-
matic Chaos,” after over three years of competition.
(3) News Articles : News services have attempted to identify articles of interest to readers,
based on the articles that they have read in the past. The similarity might be based on
the similarity of important words in the documents, or on the articles that are read by
people with similar reading tastes. The same principles apply to recommending blogs
from among the millions of blogs available, videos on YouTube, or other sites where
content is provided regularly.