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CHAPTER 9. RECOMMENDATION SYSTEMS
1. The average rating given by u on all rated movies and
2. The average of the ratings for movie m by all users who rated that
movie.
was only 3% worse than CineMatch.
• The UV-decomposition algorithm described in Section 9.4 was found by
three students (Michael Harris, Jeﬀrey Wang, and David Kamm) to give
a 7% improvement over CineMatch, when coupled with normalization and
a few other tricks.
• The winning entry was actually a combination of several diﬀerent algo-
rithms that had been developed independently. A second team, which
submitted an entry that would have won, had it been submitted a few
minutes earlier, also was a blend of independent algorithms. This strat-
egy – combining diﬀerent algorithms – has been used before in a number
of hard problems and is something worth remembering.
• Several attempts have been made to use the data contained in IMDB, the
Internet movie database, to match the names of movies from the Netﬂix
challenge with their names in IMDB, and thus extract useful information
not contained in the Netﬂix data itself.
IMDB has information about
actors and directors, and classiﬁes movies into one or more of 28 genres.
It was found that genre and other information was not useful. One pos-
sible reason is the machine-learning algorithms were able to discover the
relevant information anyway, and a second is that the entity resolution
problem of matching movie names as given in Netﬂix and IMDB data is
not that easy to solve exactly.
• Time of rating turned out to be useful. It appears there are movies that
are more likely to be appreciated by people who rate it immediately after
viewing than by those who wait a while and then rate it. “Patch Adams”
was given as an example of such a movie. Conversely, there are other
movies that were not liked by those who rated it immediately, but were
better appreciated after a while; “Memento” was cited as an example.
While one cannot tease out of the data information about how long was
the delay between viewing and rating, it is generally safe to assume that
most people see a movie shortly after it comes out. Thus, one can examine
the ratings of any movie to see if its ratings have an upward or downward
slope with time.
9.6
Summary of Chapter 9
✦Utility Matrices: Recommendation systems deal with users and items.
A utility matrix oﬀers known information about the degree to which a
user likes an item. Normally, most entries are unknown, and the essential
