Netflix Employs Human “Taggers” to Add Metadata to Suggestion Database
Every week, [Jordan] Canning receives a list of movies and TV shows. Usually there are about five, ranging from Quebecois preschool shows to crazy violent Sci-Fi flicks.
She watches each with a spreadsheet open on her laptop and notes every detail imaginable in the film. Does it end tragically or have a happy one? Was there a high squirm factor? What about the use of curse words?
“It covers everything from big picture stuff like storyline, scene and tone, to details of whether there is a lot of smoking in the movie,” Canning says.
Each Netflix entry in the massive Netflix library is tagged with north of 100 data points. Some are simple, like the gender and jobs of the main characters. Others are ratings, like how violent is the title on a scale of one to five?
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About Gary Price
Gary Price (firstname.lastname@example.org) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.