Improving TV Experience with Personalized Content Recommendations
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ImpressTV provides personalized recommendations, data analytics, and targeted advertising for IPTV and OTT systems. They face challenges in unifying heterogeneous data from different sources and devices, dealing with cold start problems and lack of explicit user data, and accounting for context dependencies. ImpressTV uses tensor factorization techniques to model user, item, and temporal factors for recommendations. Case studies show their algorithms increase video revenue and engagement by improving content discovery.
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Improving TV Experience with Personalized Content Recommendations
- 1. 1 IMPROVING THE TV USER EXPERIENCE BY ALGORITHMS: PERSONALIZED CONTENT RECOMMENDATION FOR IPTV AND OTT SYSTEMS Dávid Zibriczky, ImpressTV 2015-10-09
- 2. 2 Who we are? • Acquisition of IPTV, OTT and media business line of Gravity R&D (July 2014) • Technical Centre in Budapest, sales and management from the UK What we are doing? • Providing personalized recommendations, data analytics, targeted advertising and audience measurement for corporate clients • Main domains: Video-On-Demand, LinearTV, OTT, Advertisements About ImpressTV
- 3. 3 TV is dead? 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) • TV and radio are not dead • Temporary fall in 2008 • Newspapers, magazines and yellow pages are in falling trend in 2000s
- 4. 4 • 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 Then and Now
- 6. 6 What is a Recommender System?
- 7. 7 • For the consumers › Content discovery (relevance, time, information filtering) › Exploring new preferences (habits, engagement) • For the business › Improving KPIs, balancing consumption (long tail contents) › Promotions, targeting, campaign, analitics, reporting • For the vendors › Data integration, insights, data science, optimization › Technology challenges, deployment, maintenance What does a Recommender System mean in TV business?
- 8. 8 • Goals for consumers not necessarily equal to business! • Simplified example 1: YouTube (User-generated contents) › Goal of the business: Receive more income from advertisements › Goal of the users: Having useful time by watching videos › The goal is realized in the same way, more videos watched are good for both. • Simplified example 2: Netflix (Video On Demand rental) › Goal of the business: Increase the income of VOD › Goal for the users: Watching interesting movies, spending less time for searching › The goal is different. Netflix wants the users pay more. The users basically don’t want to spend more. Difference between the goals
- 9. 9 Challenges
- 10. 10 Evolution of the Recommender Problem Source: http://www.slideshare.net/xamat/recsys-2014-tutorial-the-recommender-problem-revisited
- 12. 12 Device-dependency Source: B. Kitts - Tectonic Shifts in Television Advertising (RecSys 2014) 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 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+ TraditionalTV Traditional TV 12-17 year olds 25-34 year olds 18-24 year olds 65+ year olds
- 13. 13 Unification challenges PVR LiveTV CatchUp SVoD TVoD ADs Devices Social Networks Metadata sources TV contents (operators)
- 14. 14 • Who is the front of the TV? (watching behavior pattern recognition) • Cold-start problem, opt-out users (metadata enrichment, hybrid filtering) • Noise filtering, lack of explicit data (filtering and weighting method, implicit feedbacks) • Context-dependency (device, time, mood) • Cross-domain recommendation, content centralization • Time-dependent recommendable set (model-based methods) • Popularity effect (diversification) • Upselling, subscription (Live VOD) • Offline vs. Online KPIs Data Science Challenges
- 15. 15 • Heterogeneous external data sources (data unification) • Big data (scalable algorithms, distributed databases) • Latency in data transfer, streaming service • Responsivity, load, SLA (distributed systems, load balancer) • Maintenance, service, follow-up • Open source vs. self-development • Distributed systems vs. single server • Software-as-a-service vs. on-site deplyoment Technology Challenges
- 16. 16 Let’s do some math…
- 17. 17 • Different preferences during the day • Context: Time period • Time period 1: 06:00-14:00 Tensor Factorization R1 Item1 Item2 Item3 … User1 1 … User2 1 0 … User3 … …. … … … …
- 18. 18 R1 Item1 Item2 Item3 … User1 1 … User2 1 0 … User3 … …. … … … … R2 Item1 Item2 Item3 … User1 0 1 … User2 1 … User3 1 … …. … … … … Tensor Factorization • Different preferences during the day • Context: Time period • Time period 2: 14:00-22:00
- 19. 19 R1 Item1 Item2 Item3 … User1 1 … User2 1 0 … User3 … …. … … … … R2 Item1 Item2 Item3 … User1 0 1 … User2 1 … User3 1 … …. … … … … R3 Item1 Item2 Item3 … User1 1 … User2 … User3 1 1 … …. … … … … Tensor Factorization • Different preferences during the day • Context: Time period • Time period 3: 22:00-06:00
- 20. 20 R1 Item1 Item2 Item3 … User1 1 … User2 1 0 … User3 … …. … … … … R2 Item1 Item2 Item3 … User1 0 1 … User2 1 … User3 1 … …. … … … … R3 Item1 Item2 Item3 … User1 … User2 ො𝒓 𝑢𝑖𝑡 … User3 … …. … … … … QT q11 q21 q31 … q12 q22 q32 … P p11 p12 p21 p22 p31 p32 … … Tt11 t12 t21 t22 t31 t32 𝑹 𝑵𝒙𝑴: preference matrix 𝑷 𝑵𝒙𝑲: user feature matrix 𝑸 𝑴𝒙𝑲: item feature matrix 𝑻 𝑳𝒙𝑲: time feature matrix 𝑵: #users 𝑴: #items 𝑳: #time periods 𝑲: #features ො𝒓 𝒖𝒊t = 𝒌 𝒑 𝒖𝒌 𝒒𝒊𝒌 𝒕𝒕𝒌 𝑹 = 𝑷° 𝑸° 𝑻Tensor Factorization
- 21. 21 Does the Recommender System work?
- 22. 22 Case Study (VOD, Magyar Telekom) -5% 0% 5% 10% 15% 20% 25% 30% 01-01 01-03 01-05 01-07 01-09 01-11 01-13 01-15 01-17 01-19 01-21 01-23 01-25 01-27 01-29 01-31 02-02 02-04 02-06 02-08 02-10 02-12 02-14 02-16 02-18 02-20 02-22 02-24 02-26 02-28 A/B testing VOD Revenue increase vs. External Baseline Group (7-day Moving Avg.) ITV Algorithm 1 ITV Algorithm 2 ITV Algorithm 3 Changes in 3 Changes in 2 Changes in 2 Baseline 1
- 23. 23 • 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. Case Study (LinearTV, SaskTel) 0% 20% 40% 60% 80% 1 10 100 Average CTR vs. # of rec. requests from the first use of RS EPG/Zapp R4U Benefit Watching Length Ratio 29,90% 42,02% +40,54% Completed Watching Ratio 15,91% 30,53% +91,89%
- 24. 24 • Heterogeneous data integration, cloud • Content discovery and personalization over recommendation • Productization over optimization • Hybrid Filtering with Factorization Machines • Content popularity prediction • Mood detection, gamification • Future TV: Personalized channels for each users, schedule recommendation State of the art
- 25. 25 Presented by: Contact: THANK YOU! www.impresstv.com David Zibriczky Head of Data Science david.zibriczky@impresstv.com