[OPLIN 4cast] OPLIN 4cast #367: Finding good flicks

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OPLIN 4Cast

OPLIN 4cast #367: Finding good flicks
January 8th, 2014

Netflix laptopEvery 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 /The Atlantic/ describes how Netflix does 
that, while the other articles are reactions to the /Atlantic/ article. 
It's interesting to see how someone else handles the "what do I 
read/watch next" question.

  * How Netflix reverse engineered Hollywood
    <http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/>
    (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."
  * 76,897 genres and 1 very human touch: How Netflix knows you best
    <http://www.dailydot.com/business/netflix-metadata-tags-genre-recommends/>
    (The Daily Dot/Beejoli Shah) "Given its recent successful dips into
    original programming with /House of Cards/ and /Orange Is The New
    Black/, 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."
  * Netflix's dumbed-down algorithms
    <http://blogs.reuters.com/felix-salmon/2014/01/03/netflixs-dumbed-down-algorithms/>
    (Reuters finance blog/Felix Salmon) "While Amazon has orders of
    magnitude more books than your local bookseller ever had, Netflix
    probably has /fewer/ 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 /it thinks you'll like/,
    rather than simply looking for the specific movies which it knows
    (because you told it) that you /definitely want to watch/. This,
    from a consumer perspective, is not an improvement."
  * Marketing lessons from the Netflix's micro-genre generator
    <http://www.marketingpilgrim.com/2014/01/marketing-lessons-from-the-netflixs-micro-genre-generator.html>
    (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."

*/Burr fact:/*

For some reason that is a mystery to Netflix, their data places /Perry 
Mason/ stars Raymond Burr and Barbara Hale among the top ten favorite 
actors/actresses.
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