Scale at Speed™
English (Global)
French (Canada)
Abstract
Industry data suggests that 49% of viewers are willing to cancel a service because they cannot find relevant content quickly. Existing recommendation systems act as mirrors, reflecting a user's past behavior but are incapable of inferring the user’s future intent. This paper outlines how foundation models can enhance existing recommendation architectures, enable intent-aware discovery while avoiding a disruptive full-system replacement.
Key Insights
Move Beyond Historical Matching
Legacy ML models act like mirrors, reflecting past behavior rather than anticipating future intent.
Build a Unified Preference Layer
By evolving the fragmented, siloed brain into a shared preference-learning layer, streaming platforms can address the “14-minute leak” at its source while continuing to use proven retrieval and ranking systems where they work best.
Ground Personalization in the Catalog
Ensures content suggestions are grounded in the actual content library and continue to respect availability, rights, safety, editorial, and business constraints.
Operationalize the Unified Brain
Platforms need to balance data readiness, risk mitigation, cold-start handling, and the economics of token and embedding inference.
