Surfacing the Right Videos for Each Viewer with Adobe Primetime Recommendations

Every provider of a multiscreen TV experience shares a common challenge, which is to highlight the videos that are the most likely to appeal to each user. Today, we’re introducing Adobe Primetime Recommendations, enabled by Adobe Target, to address this challenge. Adobe Primetime Recommendations allows customers of our multiscreen TV platform to use machine learning to intelligently surface data-driven, personalized video recommendations that play back instantly. This is just one of many ways that Adobe is using machine learning to amplify human intelligence.

Adobe Primetime Recommendations helps our customers increase viewer engagement with their sites, apps, and digital marketing content through:

  • Better Data and Leading Algorithms - Adobe Primetime Recommendations uses a massive data repository and multiple industry-leading personalization algorithms from Adobe Target to make the best, most relevant video recommendations for each user.
  • Continuous Optimization — Adobe Primetime Recommendations helps customers identify the best algorithms to make video recommendations and the best layouts to display video recommendations through the use of A/B and multivariate testing.
  • Instant Playback — Adobe Primetime Recommendations provides an instant playback experience by telling Adobe Primetime TVSDK which recommended videos to preload with Instant on.

We’re excited to see how these advantages of better data, leading algorithms, continuous optimization, and instant playback help our customers produce the next generation of TV recommendations.

Lakshmi Shivalingaiah

About Lakshmi Shivalingaiah

Lakshmi Shivalingaiah is a Senior Product Manager for the Adobe Primetime team, responsible for driving the product strategy for Adobe’s personalized TV video technology stack. She has an MBA from Haas School of Business in Berkeley. She has over 6 years of technical experience in mobile and web application development, customer solution and GTM strategy and business case modelling in both startups and mature environments. She loves hiking, Bollywood dancing, and wine tasting in her spare time.

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