This document discusses trends in television and entertainment consumption and recommendations. It notes that viewers now spend more time with media across hundreds of channels and streaming services. Recommender systems aim to help users find relevant content while improving business metrics for media companies. The document outlines challenges in recommending linear TV, video-on-demand, and content across different devices. It provides examples of how recommendations can increase viewing time and engagement for different content platforms. Future trends may include more personalized recommendations and discovery across cloud-based content libraries.

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IMPRESSTV
Predictive Solutions and Analytics for TV & Entertainment Businesses
2015.02.11.

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Consumption Trends
• More time spent watching media contents than ever
• Hundreds of channels, ten thousands of movies, millions of user uploaded videos
• Heterogeneous mixture of devices and contents
• Massive consumptional information
• Cloudization and centralization

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• For the consumers (users)
› Helping the users to find useful contents to satisfy their needs
› Reducing the time of content searching
› Providing relevant information from the massive information flow
› Exploring new preferences, user journey
› Trust in recommender system, changing habits
• For the business clients
› Improving business success indicators (revenue, watching time, upselling, churn)
› Reducing popularity effect, less popular contents are also consumed
› Promotions, targeting, campaign
› Analitics, reporting
Goals and benefits of Recommender Systems in teclo business

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• Basic principles of recommendation theory
• Optimization for Rating prediction problem
• Model-based Collaborative Filtering algorithms (SVD++, RBM, BRISMF, ALS)
• Huge collection of algorithms designed for explicit data sets
• Ratings are more valuable than metadata
• One method is not enough
• Time matters
• Popularizing Recommender Systems
Legacy from Netflix Prize

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What’s next?

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Video On Demand

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Content types / Video On Demand (Set-top-box)

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Content types / CatchUp (Over-the-top)

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Box Office vs. rating
Source: The Hollywood Quantifier

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Evolution of the Recommender Problem
Source: http://www.slideshare.net/xamat/recsys-2014-tutorial-the-recommender-problem-revisited

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• Goals for consumers not necessarily equal to business!
• Simplified example 1: YouTube (free contents)
› Goal of the business: Receive more income from advertisements
How: More videos watched, more advertisements seen
› Goal of the users: Having entertaining/useful time by watching videos
How: Using search engine, clicking on recommendations
› The goal is realized in the same way, more videos watched are good for both.
• Simplified example 2: Netflix (DVD/Blu-ray or Video On Demand rental)
› Goal of the business: Increase the income
How: More (expensive) contents, improving user engagement
› Goal for the users: Watching interesting movies, spending less time for searching
How: Using recommendation engine to make it easier
› The goal is different. Netflix wants the users pay more. The users basically don’t
want to spend more.
Difference between the goals

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• Lack of explicit data (implicit algorithms)
• Difference between offine measures and business success indicators (A/B testing)
• Popularity effect (diversification)
• Weak user activity, mood detection (time-based modeling, cross-domain recommendation,
diversification)
• Quality of metadata (metadata enrichment)
• Weak user penetration on VOD consumption (cross-domain recommendation)
• User cold start (Linear  VOD)
Challenges of VOD/CatchUp recommendation

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Linear TV

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Consumption Trends
4,000
40,000
1952 1962 1972 1982 1992 2002 2012
Radio Direct Mail Yellow Pages
Magazines Newspapers TOTAL TV
Source: B. Kitts - Tectonic Shifts in Television Advertising (RecSys 2014)

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Content types / Linear (Electronic Program Guide)
Source: http://bigscreenglobal.com/

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Time dependency
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0.00
0.50
1.00
1.50
2.00
2.50
3.00
K2-11 T12-17 A18-24 A25-34 A35-44 A45-54 A55-64 A65+
Video on Mobile
Video on Mobile
0.00
0.50
1.00
1.50
2.00
2.50
K2-11 T12-17 A18-24 A25-34 A35-44 A45-54 A55-64 A65+
Video on internet
Video on internet
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
K2-11 T12-17 A18-24 A25-34 A35-44 A45-54 A55-64 A65+
Time-shiftedTV
Time-shifted TV
0.00
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1.00
1.20
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K2-11 T12-17 A18-24 A25-34 A35-44 A45-54 A55-64 A65+
TraditionalTV
Traditional TV
12-17 year olds
25-34 year olds
18-24 year olds
65+ year
olds
Device dependency
Source: B. Kitts - Tectonic Shifts in Television Advertising (RecSys 2014)

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• Who is the front of the TV? (watching behavior pattern recognition)
• Data interpretation (what kind of information we can gather from the feedbacks)
• Noise (filtering and weighting method)
• Cold-start problem (metadata enrichment, hybrid filtering)
• Device-dependency, time-dependency (context-aware methods)
• Big data, scalability (scalable algorithms, user-retrain methods)
• Time-dependent recommendable set (model-based methods)
• Feedbacks-recommendations for channels, but via contents (data processing)
• Cross-domain recommendation difficulties
• What is the best Key Performance Indicator?
Challenges of Linear content recommendation

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User Interface Concept

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Does it work?

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Case Study Results
0%
20%
40%
60%
80%
1 10 100
Average CTR vs. # of rec. requests
from the first use of R4U
• Increasing trust in recommender system
• Contents selected via R4U are watched 40% longer and completed with almost twice more
probability than in standard way.
• High correlation between Recall/MRR and Completed Waching Ratio
Source: D. Zibriczky, Z. Petres, M. Waszlavik and D. Tikk: EPG content recommendation in large scale: a case study on interactive TV
platform. Machine Learning with Multimedia Data – Special session at the 12th IEEE International Conference on Machine Learning and
Applications (ICMLA'13), Miami, Florida, 2013.
EPG/Zapp R4U Benefit
Watching Length
Ratio
29,90% 42,02% +40,54%
Completed
Watching Ratio
15,91% 30,53% +91,89%

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Global trend
• Hybrid Filtering
• Content — Device cloud, Content discovery („There are no bad shows, just shows with small
audiences”)
• Echo chambers
• Incorporation of personalized recommendations, search and advertising in products
• Future TV: Personalized channels for each users, schedule recommendation
Future

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Presented by:
Contact:
THANK YOU!
www.impresstv.com
David Zibriczky
Head of Data Science
david.zibriczky@impresstv.com