322
CHAPTER 9. RECOMMENDATION SYSTEMS
The Long Tail
Figure 9.2: The long tail: physical institutions can only provide what is popular,
while on-line institutions can make everything available
suggestions of products that they might like to buy. These suggestions are
not random, but are based on the purchasing decisions 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 dollars 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
Pragmatic Chaos,” after over three years of competition.
3. News Articles: News services have attempted to identify articles of in-
terest 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.
1To be exact, the algorithm had to have a root-mean-square error (RMSE) that was 10%
less than the RMSE of the Netﬂix algorithm on a test set taken from actual ratings of Netﬂix
users. To develop an algorithm, contestants were given a training set of data, also taken from
actual Netﬂix data.
