Recommendation Systems - Mining of Massive Datasets - page 292

9
Recommendation Systems
There is an extensive class of Web applications that involve predicting user responses to op-
tions. Such a facility is called a recommendation system . We shall begin this chapter with a
survey of the most important examples of these systems. However, to bring the problem into
focus, two good examples of recommendation systems are:
(1) Offering news articles to on-line newspaper readers, based on a prediction of reader in-
terests.
(2) Offering customers of an on-line retailer suggestions about what they might like to buy,
based on their past history of purchases and/or product searches.
Recommendation systems use a number of different technologies. We can classify these
systems into two broad groups.
• Content-based systems examine properties of the items recommended. For instance,
if a Netflix user has watched many cowboy movies, then recommend a movie clas-
sified in the database as having the “cowboy” genre.
• Collaborative filtering systems recommend items based on similarity measures
between users and/or items. The items recommended to a user are those preferred
by similar users. This sort of recommendation system can use the groundwork laid
in Chapter 3 on similarity search and Chapter 7 on clustering. However, these tech-
nologies by themselves are not sufficient, and there are some new algorithms that
have proven effective for recommendation systems.
9.1 A Model for Recommendation Systems
In this section we introduce a model for recommendation systems, based on a utility matrix
of preferences. We introduce the concept of a “long-tail,” which explains the advantage of
on-line vendors over conventional, brick-and-mortar vendors. We then briefly survey the
sorts of applications in which recommendation systems have proved useful.