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.
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.