OTT Recommendation Engine
On a streaming platform, discovery is retention. A viewer who can't find their next thing to watch cancels — which is why recommendations drive the majority of viewing on the platforms that do them well. A real recommendation engine personalizes the home screen, powers continue-watching and "because you watched" rows, ranks search, and learns from behavior in near real time.
Apexnova builds custom AI recommendation engines you own outright — personalized rows, continue-watching, similar-title and trending models, and search ranking that improves with every session — production-ready in 30 days, wired into your platform and your data.
Streaming apps live in the stores today.
Built end to end by our team and owned by the client. Each case study covers the product decisions and the engineering behind them. All case studies
Microdrama appBinj TV
4.97★ Google Play rating · 1.28k Reviews · 200k+ Installs
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Subscription OTT appWatchStar
130k+ Downloads · 3.9★ Google Play rating
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What a recommendation engine actually does
Personalized home screen. Rows ordered per viewer from watch history, completion, and interaction signals — so the first screen is relevant, not the same catalog for everyone.
Continue-watching and "because you watched." The highest-converting rows in streaming, resurfacing in-progress titles and close matches to keep sessions going.
Similar-title, trending, and cold-start. Content-based similarity for new items and new users (where collaborative data is thin), plus trending and editorial blends so nothing sits undiscovered.
Search ranking and A/B testing. Personalized search results and an experimentation framework to measure lift on watch-time and retention, so the models are tuned to your goals — not a generic black box.
Custom engine vs generic add-on
A rented recommendation widget optimizes for its own defaults and keeps your behavioral data. A custom engine optimizes for your KPIs and keeps the data with you.
| Aspect | Generic add-on | Custom engine (Apexnova) |
|---|---|---|
| Optimizes for | Vendor defaults | Your watch-time / retention KPIs |
| Your data | Held by the vendor | Stays with you, trains your models |
| Tuning | Limited knobs | Fully tunable, A/B tested |
| Cold-start | Often weak | Content-based + editorial blend |
What you get
A custom recommendation and personalization engine built into your platform — personalized home rows, continue-watching, similar-title, trending, cold-start handling, personalized search ranking, and an A/B testing framework, with analytics tying recommendations to watch-time and retention. Built into a new platform or added to an existing one. Your models, your data — no per-request black box.
Why Apexnova for recommendations
- Tuned to your KPIs. Optimized for your watch-time and retention goals, measured by built-in A/B testing.
- Your data stays yours. Behavioral data trains your models, not a vendor's.
- Strong on cold-start. New titles and new users get relevant recommendations from day one.
- 30-day delivery, retrofit-friendly. Add it to an existing platform without a rebuild.
Frequently asked questions
What is an OTT recommendation engine?
An OTT recommendation engine personalizes what viewers see — the home-screen rows, continue-watching, similar-title and trending suggestions, and search ranking — using behavioral signals, to improve discovery and retention. Apexnova builds custom AI recommendation engines wired into your platform, production-ready in 30 days.
How do recommendations affect retention?
Discovery is one of the strongest drivers of retention: viewers who quickly find their next title keep watching and stay subscribed, while those who can't churn. Well-tuned recommendations increase watch-time per session, which is why they power the majority of viewing on leading platforms.
How do you handle new titles and new users (cold-start)?
We combine content-based similarity — using metadata and attributes so new titles and new users get relevant suggestions before behavioral data exists — with trending and editorial blends, so nothing sits undiscovered and new viewers get a good first experience.
Do I own the model and the data?
Yes. Unlike a rented widget, the engine and the behavioral data that trains it stay with you, tunable to your KPIs and measurable with a built-in A/B testing framework.
Turn your catalog into watch-time
Tell us your catalog and retention goals, and we'll return a fixed-scope quote with a 30-day plan for a recommendation engine tuned to your platform.