Optimize Streaming Search with Metadata & Machine Learning
▼ Summary
– A panel titled “Finder’s Feat” at Streaming Media Connect will discuss optimizing content discovery using metadata and machine learning.
– The panel, moderated by consultant Rebecca Avery, will explore how these tools improve relevance, engagement, and revenue for streaming services.
– Panelist Tony Huidor argues the industry must move beyond legacy metadata to new types that support AI-powered search based on viewer mood.
– Another panelist, Jamie Mackinlay, notes that while metadata volume is exploding, quality and consolidation challenges hinder machine learning efforts.
– Panelist Tom Gennari emphasizes the future of discovery involves real-time, availability-aware data that predicts viewer behavior, not just static recommendations.
In today’s crowded streaming landscape, viewers often spend more time searching for something to watch than actually watching. This discovery challenge directly impacts revenue, making the intelligent use of metadata and machine learning a critical business imperative. A forthcoming industry panel will delve into how these technologies transform search from a user frustration into a powerful driver of engagement and monetization.
The discussion, titled “Finder’s Feat: Optimizing Search & Discovery With Metadata & Machine Learning,” will be moderated by Rebecca Avery. As the owner of Integration Therapy and chair of the Streaming Video Technology Alliance’s Metadata Working Group, Avery brings over two decades of media operations expertise. She emphasizes that structured data and AI are key to making standout content visible. The session promises practical insights from leading companies on implementing AI-powered personalization that respects user privacy while boosting ad performance and viewer retention.
Tony Huidor of Cineverse will argue for a fundamental shift in how the industry approaches metadata. He points out that while distribution models have evolved from physical media to streaming, discovery tools have stagnated. Huidor advocates moving beyond basic descriptive tags to capture more nuanced elements, like the mood or emotional experience a viewer seeks. This richer data layer is essential for training the sophisticated recommendation engines that modern audiences expect.
Jamie Mackinlay, founder and CEO of SUMM8, will address the operational hurdles. He notes that the sheer volume of available metadata, including new AI-generated data, presents both an opportunity and a significant quality control problem. Inconsistent or poorly structured data from various sources can cripple efforts to apply machine learning effectively, making consolidation and standardization a necessary first step for any successful discovery strategy.
Adding to the conversation, Tom Gennari, Chief Data Officer at Fabric, will explore the next frontier: context-aware discovery. He suggests the future is not just about suggesting what to watch, but understanding where it’s available, how it can be accessed, and the viewer’s immediate context. By merging deep, adaptive metadata with real-time availability data, platforms can begin to predict viewing behavior, creating a more seamless and proactive user experience.
This gathering of experts will provide a comprehensive look at the tools and strategies needed to thrive in the competitive streaming market. The consensus is clear: investing in advanced metadata frameworks and machine learning is no longer optional for services that wish to keep audiences engaged and unlock new revenue streams.
(Source: Streaming Media)
