A good content recommendation system is key for any video content provider. Machine Learning video recommendations provide a unique opportunity for broadcasters, Pay-TV operators, TV Networks, and any content distributor to increase engagement and reduce churn through content personalization. https://www.bigdataspain.org/2017/talk/video-recommendations-and-machine-learning Big Data Spain 2017 16th - 17th Kinépolis Madrid

2. JUMPActionable video insights
Video recommendations and
machine learning

3. WHO AM I? ☺
• MSIE in Industrial Engineering (Universidad
Politécnica Madrid)
• Executive Master’s in Growing Companies @
Stanford University
• Data Science studies @ MIT
• Helping video service providers since 2000
• Currently CEO at JUMP (Data Driven Video)
• Data Lover!

4. JUMP offers a unified
tool that in addition to
providing fast insights
into “what has
happened”, “what is
happening” and “what
will happen” on video
services, it makes an
instant impact on
business results.
WHAT AM I DOING? ☺

5. 4
The Video Industry

6. 5Video Industry

7. “By 2019, 80% of global Internet consumption will be video content”
The Video Streaming Market
• The global video streaming
market is projected to grow
from USD 30.29 Billion in 2016
to USD 70 Billion by 2021

8. THREE SUCCESS FACTORS IN A VIDEO SERVICE
1

11. 2
THREE SUCCESS FACTORS IN A VIDEO SERVICE

14. 3
THREE SUCCESS FACTORS IN A VIDEO SERVICE

15. 14
Video
recommendations

16. • Households are watching up to 120 minutes
more TV per week.
• Services are able to increase user traffic by a
factor of up to 10.
• 25% increase of buy-rates in VoD
• 8% increase in total ARPU
WHY USE MACHINE LEARNING FOR VIDEO
RECOMMENDATIONS?
Business performance

17. User engagement & satisfaction
• Users create on average 7 to 14 profiles per
household.
• Users enjoy previously unknown content.
• Users want even more content to feed
their personal profiles.
• After less than 3 weeks, up to 80-90% of the
viewing time is served by video
recommendations.
WHY USE MACHINE LEARNING FOR VIDEO
RECOMMENDATIONS?

18. MACHINE LEARNING RECOMMENDATION
ALGORITHM EXAMPLE
1. Considers the user’s view history, and optionally, the
time of day.
2. Creates a "user preference vector“ to represent how
much of each genre the user has seen; if the time of day
is provided as a parameter, more weight is given to
those movies watched at the given time of day.
3. Compiles a list of similar movies, selecting the 10
movies most similar to those watched by the user.
Movies that have already been seen are discarded. The
remaining are the candidate recommendations.

19. 4. Each candidate movie is assigned a score,
computed by taking into account the user's genre
preference and the movie’s genre. The higher the
score, the closer it will be to the user’s preferences.
5. A recommendation is produced by taking the top x
movies that scored the highest in (4)
6. The popularity of the movie within the video
service, in the form of total number of views, is used
for tie-breaking purposes.
MACHINE LEARNING RECOMMENDATION
ALGORITHM EXAMPLE

20. R&D NEW OPPORTUNITIES IN MACHINE
LEARNING RECOMMENDATIONS
1. AUDIO/VIDEO RECOGNITION FOR
METADATA ENRICHMENT
1. SOCIAL RECOMMENDATIONS
1. LINEAR TV/SMART EPG

21. • The creation of a neuronal network to enrich the content metadata,
carrying out a comparative analysis with the tagging of audio, video,
and audio and video together with the goal of a more efficient solution.
• Additionally, the analysis of audio only (dimensionality reduction) could
yield results similar to those resulting from video or combined video and
audio analysis, thereby drastically improving the recommendation
process.
• The approach could tag content attributes such as:
- Image velocity
- colours and luminosity
- localization
- times of day within the video
- emotion detection
- objects that appear in the video
1. AUDIO/VIDEO RECOGNITION FOR METADATA
ENRICHMENT
E.G: Movie image recognition using AI
for automatic movie tag generation

22. 2. SOCIAL RECOMMENDATIONS
• Friends are the most powerful source of
information
• Friends have a high level of trust
• Very likely to follow friends’
recommendations
• Creating a recommendation model
that includes the user’s social
network when recommending
content expands the limits of
personalization getting closer and
closer to a personalized discovery
experience.

23. • No personalization in linear TV
interface
• Difficult to decide what to watch from
+100 channels
• Creating a recommendation model
that factors in data regarding the
channels the user watches at a
given time, the programs that are
the most interesting at a given time
of day, together with an EPG display
personalized according to this data,
will create a much more personalized
linear TV watching experience.
3. LINEAR TV/SMART EPG

24. • There is huge potential for Machine Learning in video
recommendation/personalization with a proven
impact on business results
• We are just in the early stages of what might be done to
personalize entertainment
• Data Driven video technology is disrupting the video
market
- Recommendations/personalization
- Acquisition through trial conversion predictions
- Retention though churn analysis
- Audience clustering
- Advertisement pressure optimization
- and much more …
SUMMARY

25. info@jumptvs.com

26. “Thanks!”
Jerónimo Macanás
CEO
jero@jumptvs.com
+34 605 938 091
www.jumptvs.com