PERSONALIZED STREAMING WITH A CONTEXTUAL ADVERTISMENT RECOMMENDATION ENGINE

SceneFuse

AI & Computer Vision

Dynamic Ad Insertion

Personalized Experiences

Platform Monetization

01 Problem Statement

Current ad-supported streaming relies on interruptive formats that cut away from the content to show standalone ads. This breaks immersion and leads to measurable drops in watch time and engagement. At the same time, most platforms underutilize user behaviour data, delivering generic content feeds and broadly targeted ads that fail to reflect individual preferences. The absence of a deeply personalized recommendation system results in irrelevant content discovery and repetitive, poorly matched advertising—reducing attention, recall, and overall brand effectiveness.

Frequent ad breaks cause frustration and drive users toward ad-blockers or competing platforms.
Most platforms deliver generic content feeds with limited user-specific recommendations, making content discovery inefficient and reducing long-term engagement.
Ads are often repetitive and poorly aligned with user interests or content genre, leading to low attention, weak recall, and diminished brand impact.
The lack of a unified system that combines personalized recommendations with non-disruptive advertising prevents platforms from optimizing user experience, advertiser performance, and creator monetization simultaneously.

02 Proposed Solution

Replace interruptive ad breaks with in-scene embedded advertisements that seamlessly transform existing objects walls, billboards, screens, vehicles, clothing, and digital displays into dynamic, context aware ad placements, personalized through Intelligent Recommendation systems that adapt to each viewer’s interests and viewing context, all while the video plays completely uninterrupted.

A user-specific recommendation system analyses watch time, genre preferences, and engagement behaviour to deliver highly personalized content feeds.
Advertisements are embedded directly into the video scene itself, appearing naturally on existing visual elements such as walls, billboards, screens, vehicles, clothing, and digital displays.
Video playback remains fully continuous, eliminating disruptive pre-roll and mid-roll ad interruptions.
A single master video can support multiple advertising campaigns through dynamic, in-scene ad overlays.
Different viewers can see different, genre-aligned advertisements within the same video scene, based on their preferences and behaviour.

04 Summary

03 Summary

This proposal presents a next-generation video streaming with advertising platform that eliminates disruptive pre-roll and mid-roll ads by seamlessly embedding branded content directly into objects within the video scene. In parallel, the platform incorporates an intelligent recommendation system that analyses user viewing behaviour.

By aligning genre-specific recommendations with contextually and behaviourally relevant embedded advertisements, the platform ensures a non-intrusive, personalized viewing experience while enabling brands to reach precisely targeted audiences within content they already engage with.

This dual approach enhances viewer satisfaction, improves ad effectiveness, sustains and potentially increases advertising revenue, and unlocks scalable monetization opportunities for content creators and advertisers alike.