IEEE Technology and Society Magazine - December 2016 - 41

not reflect their values and beliefs. These people, who
tend to lean toward liberal political positions, gravitate
toward things that feel foreign. It begs the question: how
can an algorithm be created that predicts an unexpected pleasure for someone?

Netflix's Cinematch System
It appears that teams tried to solve this problem in the
public contest that Netflix held to improve their Cinematch recommendation system by 10%. The stakes
were high: In 2014, Netflix estimated that if their recommendation engine improved recommendations by 10%
and avoided 1% of cancelations, it would be worth $500
million in extra revenue.
Many of the contestants were using a mathematical
technique called "singular value decomposition," which
identifies groups of films that share a certain predictive
factor such as graphic violence or profanity or "sci-fi
geekiness" or "chick-flickiness." The most disarming
thing about this technique is that programmers do not
identify the factor beforehand. They run the algorithm
and then hope they can figure out what "factor" the program has isolated in each group. Sometimes the answer
seems very clear but other times it is very mysterious.
This process may reveal a rather chaotic element in
human taste: that many people appreciate precisely
what their viewing history would not reflect - something
unexpected, surprising, or new.
It is likely that Netflix discovered similar issues in its
more recent effort to combine human and machine coding to generate "personalized genres" for its subscribers. An enterprising reporter from the The Atlantic,
Alexis Madrigal, recorded 76 897 "altgenres" as they
call them internally at Netflix [4]. After consulting with
the architect of the system, Todd Yellin, Madrigal discovered that he, too, was stymied by some of the strange
genres that emerged, such as:
■
■
■
■
■
■
■
■

Research suggests that there is a
strong link between the way people
entertain themselves and the way
they lead their lives.

Perry Mason's secretary. It raises some questions: Is this
a sign of gender bias within the algorithm, within Hollywood, or perhaps both?
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)

Raymond Burr
Bruce Willis
George Carlin
Jackie Chan
Andy Lau
Robert De Niro
Barbara Hale
Clint Eastwood
Gene Autry
Yun-Fat Chow

DECEmbEr 2016

∕

Anthony Hopkins
Bob Hope
Cary Grant
Elvis Presley
Fred Astaire
John Wayne
Michael Caine
Roy Rogers
Sean Connery
Burt Reynolds

The vast audio-visual territory that Netflix is mapping
is reminiscent of a concept from film theory called "the
space-off." In Teresa de Lauretis' terms, it is "the space
not visible in the frame but inferable from what the
frame makes visible" [8]. One thing that the Cinematch
engine is trying to do is to move from obscurity what a
subscriber has not seen; to make visible what has been
invisible to them. But what the system makes visible is
very selective: it is not simply anything beyond a subscriber's typical film-watching frame of reference, but

Critically-Acclaimed Emotional Underdog Movies
Visually-Striking Foreign Nostalgic Dramas
Gritty Discovery Channel Reality TV
Mind-Bending Cult Horror Movies from the 1980s
Emotional Independent Sports Movies
Evil Kid Horror Movies
Visually-Striking Goofy Action & Adventure
Time Travel Movies starring William Hartnell

They were especially bemused to discover that the
system had named more genres after Raymond Burr -
the star of Perry Mason, a popular TV series in the
1950s and 1960s - than any other actor. Notably, this
vast content filtering system produced genres that did
not coalesce around female actors. Only one woman
appears within the top twenty: Barbara Hale, who played

11)
12)
13)
14)
15)
16)
17)
18)
19)
20)

Moderate
24%
Conservative
41%

Liberal
34%

Figure 1. Three distinct ideological clusters emerged from

the dataset.

IEEE TEchnology and SocIETy MagazInE

41



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