9.1. A MODEL FOR RECOMMENDATION SYSTEMS
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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 An-
derson about a book called Touching the Void. This mountain-climbing
book was not a big seller in its day, but many years after it was pub-
lished, another book on the same topic, called Into Thin Air was pub-
lished.
Amazon’s recommendation system noticed a few people who
bought both books, and started recommending Touching the Void to peo-
ple who bought, or were considering, Into Thin Air. Had there been no
on-line bookseller, Touching the Void might never have been seen by poten-
tial buyers, but in the on-line world, Touching the Void eventually became
very popular in its own right, in fact, more so than Into Thin Air.
9.1.4
Populating the Utility Matrix
Without a utility matrix, it is almost impossible to recommend items. However,
acquiring data from which to build a utility matrix is often diﬃcult. There are
two general approaches to discovering the value users place on items.
1. We can ask users to rate items. Movie ratings are generally obtained this
way, and some on-line stores try to obtain ratings from their purchasers.
Sites providing content, such as some news sites or YouTube also ask users
to rate items. This approach is limited in its eﬀectiveness, since generally
users are unwilling to provide responses, and the information from those
who do may be biased by the very fact that it comes from people willing
to provide ratings.
2. We can make inferences from users’ behavior. Most obviously, if a user
buys a product at Amazon, watches a movie on YouTube, or reads a news
article, then the user can be said to “like” this item. Note that this sort
of rating system really has only one value: 1 means that the user likes
the item. Often, we ﬁnd a utility matrix with this kind of data shown
with 0’s rather than blanks where the user has not purchased or viewed
the item. However, in this case 0 is not a lower rating than 1; it is no
rating at all. More generally, one can infer interest from behavior other
than purchasing. For example, if an Amazon customer views information
about an item, we can infer that they are interested in the item, even if
they don’t buy it.
