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RecSysTV 2016 - Time Series Effects For TV Recommendations

Diana Hu
September 15, 2016

RecSysTV 2016 - Time Series Effects For TV Recommendations

Recommendations for the television platform behave quite differently than the ones for video on demand platforms. A key difference is the dependency on television schedule and shows’ seasonality. In this talk, we will explore the time series effects that can be observed in the shows cyclicality, and more interestingly the effects of this cyclicality on users’ behavior. We’ll also go over some approaches to incorporate the time component onto different kinds of recommendations at Verizon IPTV

Diana Hu

September 15, 2016

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  1. HELLO! ◦  Currently @ Verizon Labs ◦  Formerly @ Intel

    Labs ◦  Large Scale Machine Learning & Computer Vision ◦  Scala & Spark since 2014 You can find me at: @sdianahu
  2. DATA @ IPTV VERIZON ◦ Millions of subscribers ◦ Live TV playbacks

    ◦ DVR recordings ◦ DVR playbacks ◦ Time sensitive content by TV Schedule ◦ Hundreds of channels: Long tail
  3. FEATURE ENGINEERING ◦ Day parting ◦ Weekday parting ◦ Volatility spikes ◦ Intraday volatility

    ◦ Rank across time segments for: □ Channels □ Genres □ Programs