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<p><!-- Make sure you modify the 4Cast title in this section -->
<span style="font-size: 20px; font-weight: bold;
color: rgb(0, 0, 0); font-family: arial;
line-height: 110%;">OPLIN 4cast #367: Finding
good flicks</span><br>
<!-- Make sure you modify the date of the 4Cast in this section -->
<span style="font-size: 11px; font-weight: normal;
color: rgb(102, 102, 102); font-style: italic;
font-family: arial;">January 8th, 2014</span></p>
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<p style="text-align: justify;font-size: 16px;
font-family: arial; line-height: 110%;"><img
alt="Netflix laptop"
src="cid:part4.03070203.06050608@oplin.org"
align="left" height="92" width="125">Every
librarian has been asked at one time or another to
recommend a book based on something that a reader
liked. You know the conversation: "I just read
<book title> and loved it, what else do you
have like that?" And since the Dewey Decimal
System is notoriously weak for categorizing
fiction, there are companies that sell books and
software to help librarians answer that question.
Netflix also has a process to make movie
recommendations, categorizing films into thousands
of "micro-genres" which can be almost comically
specific. The first article linked below from <em>The
Atlantic</em> describes how Netflix does that,
while the other articles are reactions to the <em>Atlantic</em>
article. It's interesting to see how someone else
handles the "what do I read/watch next" question.
</p>
<div> </div>
<ul style="text-align: left;">
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/">How
Netflix reverse engineered Hollywood</a> (The
Atlantic/Alexis C. Madrigal) "Using large teams
of people specially trained to watch movies,
Netflix deconstructed Hollywood. They paid
people to watch films and tag them with all
kinds of metadata. This process is so
sophisticated and precise that taggers receive a
36-page training document that teaches them how
to rate movies on their sexually suggestive
content, goriness, romance levels, and even
narrative elements like plot conclusiveness.
They capture dozens of different movie
attributes. They even rate the moral status of
characters."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.dailydot.com/business/netflix-metadata-tags-genre-recommends/">76,897
genres and 1 very human touch: How Netflix
knows you best</a> (The Daily Dot/Beejoli
Shah) "Given its recent successful dips into
original programming with <em>House of Cards</em>
and <em>Orange Is The New Black</em>, Netflix
may also be using its vast expanse of metadata
to make its own programming even smarter. While
metadata can't create a show, it can give
Netflix's development executives ... a distinct
advantage into viewer preferences that focus
groups just can't."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://blogs.reuters.com/felix-salmon/2014/01/03/netflixs-dumbed-down-algorithms/">Netflix's
dumbed-down algorithms</a> (Reuters finance
blog/Felix Salmon) "While Amazon has orders of
magnitude more books than your local bookseller
ever had, Netflix probably has <em>fewer</em>
movies available for streaming than your local
VHS rental store had decades ago. At least if
you're looking only in the 'short head' - the
films everybody's heard of and is talking about,
and which comprise the majority of movie-viewing
demand. So Netflix has been forced to attempt a
distant second-best: scouring its own limited
library for the films <em>it thinks you'll like</em>,
rather than simply looking for the specific
movies which it knows (because you told it) that
you <em>definitely want to watch</em>. This,
from a consumer perspective, is not an
improvement."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.marketingpilgrim.com/2014/01/marketing-lessons-from-the-netflixs-micro-genre-generator.html">Marketing
lessons from the Netflix's micro-genre
generator</a> (Marketing Pilgrim/Cynthia
Boris) "The takeaway here is that data is your
friend. Dive in and see what's really going on
with the business then craft a campaign to
either capitalize on your big sellers or push
out that old merchandise that's not moving.
People will buy anything (Visually-striking
Foreign Nostalgic Dramas) if you package it
right."</li>
</ul>
<div style="text-align: left;"> </div>
<p style="text-align: left; font-size: 20px;
font-family: arial; line-height: 110%;"><small><strong><em>Burr
fact:</em></strong></small><br>
</p>
<div style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;">For some
reason that is a mystery to Netflix, their data
places <em>Perry Mason</em> stars Raymond Burr
and Barbara Hale among the top ten favorite
actors/actresses.
</div>
<div style="text-align: left;"> </div>
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