TIME SERIES EFFECTS FOR TV RECOMMENDATIONS

Diana Hu Verizon Labs

RecSysTV, September 2016

HELLO!

○  Currently @ Verizon Labs ○  Formerly @ Intel Labs ○  Large Scale Machine Learning & Computer Vision ○  Scala & Spark since 2014

You can find me at:

@sdianahu

DATA @ IPTV VERIZON

○ Millions of subscribers ○ Live TV playbacks ○ DVR recordings ○ DVR playbacks ○ Time sensitive content by TV Schedule ○ Hundreds of channels: Long tail

TV EXPERIENCE

SOME HISTORY

first second

http://www.brucesallan.com/2012/10/13/evolution-technology-television-back-day/ http://avdesigns.com/blog/design-your-own-at-home-sports-bar-with-a-multiscreen-tv-system/

CREATURES OF HABIT

CYCLICAL PATTERNS

○ Prime time ○ Weekend binge ○ Sunday night football ○ Basketball season ○ Effect of Sitcoms ○ Cartoons ○ News

TIME OF DAY VOLUME

Time Segment Day Segment

6 MONTH TIME SERIES

GENRE EFFECTS

MAKING SENSE OF TIME Besides pretty visualizations…

NAÏVE APPROACH

FEATURE ENGINEERING

○ Day parting ○ Weekday parting ○ Volatility spikes ○ Intraday volatility ○ Rank across time segments for: □ Channels □ Genres □ Programs

UNLOCKING PREDICTIONS

TIME AS A CONTEXT Enhancing recommendations

SOME APPROACHES

USE CASES

○ Predicting viewership ○ Personalization ○ Re-Ranking ○ Adapting to TV content “shelf life” ○ Seasonality ○ Trends

ACKNOWLEDGEMENTS

○ Luis M. Sanchez ○ Humberto Corona

THANKS!

Any questions? You can find me at:

@sdianahu diana.hu@verizon.com

Also, we are hiring!