Helping users find content they love from massive and growing libraries — reducing choice paralysis and increasing the percentage of content that gets meaningful engagement.
Keeping audiences coming back daily in a market where every app, platform, and service is competing for the same finite hours of attention and entertainment budget.
Delivering lag-free live streaming, real-time chat, live voting, and synchronous interactive experiences to potentially millions of concurrent users without degradation.
Balancing multiple revenue streams — subscriptions, ads, in-app purchases, virtual goods, tipping — without degrading the user experience or creating paywall fatigue.
We use a hybrid approach combining collaborative filtering (what similar users enjoyed), content-based filtering (attributes of content the user has engaged with), and contextual signals (time of day, device, mood indicators). The system generates a taste profile for each user that evolves with every interaction — not just explicit ratings, but implicit signals like watch time, skip behaviour, and rewind patterns. We implement this using embedding models served via real-time inference APIs, with A/B testing infrastructure to continuously measure and improve recommendation quality against engagement metrics.
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