Tag: recommendation engines

Netflix's rolling out AI Discover, a new TV feature that lets subscribers choose mood-based themes and refine recommendations in real time. With viewing hours up just 2% in H1 2026 and $20 billion in planned content spending, Netflix's looking to…

Basics of Streaming breaks down what it takes to build a Netflix-like recommendation engine, from metadata and viewer behavior to ranking, experimentation, and infrastructure. The bigger business impact is how those systems shape content discovery, audience demand signals, and whether…

Skip calls for clear labeling when paid placement influences what viewers see or hear on TV home screens. Clear disclosure protects viewer trust and gives streamers cleaner attribution across paid, editorial, algorithmic, and negotiated exposure.

Basics of Streaming explains how short-form platforms use preloading, adaptive quality, and recommendation ranking to make every swipe feel instant. That speed comes at a cost, turning better ranking and smarter delivery into key levers for reducing wasted bandwidth, CDN…

Basics of Streaming explains how metadata gives video assets the identity, rights, technical instructions, and commercial context needed for distribution, discovery, advertising, and revenue tracking. Strong metadata infrastructure helps streaming companies launch content faster, automate workflows, improve recommendations, protect against…

HBO Max's testing Shorts and Ask HBO Max to help mobile viewers discover programming through personalized video clips and natural-language searches. The features could reduce time-to-play, increase title starts and saves, and give WBD stronger data on viewer intent.

Netflix’s use of generative AI across roughly 300 titles shows how deeply it’s embedded the technology into production and post-production workflows. The bigger story is economic: lower costs, faster global delivery, and more monetizable assets from every title.

Skip says that search isn’t disappearing, but the traditional search box is losing its role as the primary gateway to consumer intent. AI, recommendation engines, creator platforms, retailer apps, and streaming interfaces are turning search into an embedded concierge layer…

From the Archives revisits how Amazon’s “Customers Who Bought This Also Bought” feature helped turn recommendation engines into core internet infrastructure. Amazon’s item-to-item collaborative filtering showed that behavior could outperform metadata, creating a discovery model that now powers streaming, music,…

Kirby says media companies are overfocused on what audiences watch and underfocused on what they recommend. As discovery shifts to group chats, creators, social platforms, podcasts, and fan communities, viewers who persuade others to watch can create more value than…