The average user on a streaming platform spends more time browsing for something to watch than actually watching. That is a costly problem. Churn rises when content feels hard to find, and engagement drops when recommendations miss the mark. A well-functioning recommendation engine is not a nice-to-have - it is a business-critical component that directly affects retention, watch time, and revenue.
This article covers how modern recommendation systems are built, which technical approaches exist, how to test them, and what to consider when building or improving the system for your platform. It is written for CTOs and product managers who need to make well-informed decisions, not for someone looking for an introduction to machine learning.
Why Recommendations Are Critical for Retention
Retention is the most important metric in the streaming industry. It costs significantly less to retain an existing user than to acquire a new one, and the size of the content library rarely determines whether a user stays. What determines it is whether they finds the right content at the right moment.
Research published by Netflix shows that a user decides whether to stay in a session or quit within roughly 60 to 90 seconds. If nothing captures their interest, they closes the app. That is an extremely narrow window, and the only way to win it is to surface relevant content immediately.
The business impact of a recommendation system is measurable and concrete:
- Increased session length: Users who receive relevant suggestions continue watching longer per session.
- Higher session frequency: Good recommendations create habits. The user returns because they expects to always find something new and relevant.
- Lower churn: Platforms with strong recommendations see fewer voluntary cancellations.
- Better content utilisation: Older and niche titles that would otherwise remain hidden can find a new audience when recommended to the right segment.
This is not about showcasing the newest content or what is globally most popular. It is about understanding each individual user and matching their to the right title at the right moment.
Collaborative Filtering
Collaborative filtering is the best-known technique and rests on a straightforward premise: if two users have shown similar behaviour historically, there is a good chance they will also enjoy similar content going forward.
There are two main variants:
User-based collaborative filtering identifies users who resemble the target person and recommends what those similar users have enjoyed. The weakness is that it requires a large user base to perform well and becomes computationally expensive at scale.
Item-based collaborative filtering looks instead at similarities between content objects based on how they are consumed across the user base. If many users who watched title A also watched title B, then B is a strong recommendation for someone who just finished A. This approach scales better and is easier to keep updated in real time.
A common implementation pattern is matrix factorisation, where a large sparse matrix of users and content is decomposed into latent factors. Techniques such as ALS (Alternating Least Squares) and SVD (Singular Value Decomposition) are widely used for this. The result is vectors representing both users and content in the same latent space, making relevance computation straightforward.
The cold-start problem is collaborative filtering's most significant weakness. A new user with no history, or a new content item with no viewing data, is difficult to place in the system. This requires complementary methods.
Content-Based Filtering
Content-based filtering takes a different approach. Rather than comparing behaviours, it focuses on the properties of the content itself - genre, country of production, cast, director, keywords, tone - and matches those against the attributes the user has historically shown interest in.
The key advantage is that this method works without depending on other users' behaviour. A new title can be recommended immediately based on its metadata, and a new user can receive relevant suggestions after just a few interactions - especially if preferences are gathered during onboarding.
The challenge is metadata quality. Poorly structured or inconsistent content tagging directly undermines recommendation quality. Many platforms underestimate how much ongoing work is required to maintain a clean and rich content metadata base.
A more sophisticated approach is the use of semantic embeddings. Rather than relying solely on manually assigned tags, models are trained to understand the actual character of content from text - synopses, reviews, dialogue - and represent it in vector space. This enables more nuanced matching and captures properties that are difficult to tag manually, such as tone, pacing, or emotional register.
Hybrid Models: The Practical Choice for Production Environments
In practice, most mature streaming platforms choose a hybrid model that combines collaborative filtering and content-based methods, often with additional layers for contextual signals and business rules.
There are several ways to combine the approaches:
Weighted hybrid: Calculate scores from both methods and combine them with defined weights. Simple to implement and easy to adjust, though finding the right balance can be difficult.
Switching hybrid: Select the method based on what is appropriate for the situation. For new users, use content-based filtering. Once sufficient behavioural data exists, switch to collaborative filtering.
Feature-level integration: Use attributes from both user behaviour and content properties as inputs to a shared model, often a neural network. This provides more flexibility and typically better precision, but requires more engineering effort.
Cascade model: Begin with a fast, coarse ranking of a large content pool, then refine with a more sophisticated model. This is a common pattern for handling large catalogue sizes efficiently.
Beyond the core algorithms, a hybrid model should account for contextual signals such as time of day, device, location, and whether the user is watching alone or with others. A user opening the app on their phone in the morning likely has different preferences in that moment than when they sits down in front of the TV on a Friday evening.
A/B Testing Recommendations
It is impossible to know whether a recommendation system is performing well without measuring it. And it is impossible to measure without a structured testing framework.
A/B testing recommendations differs from typical interface A/B testing in one important way: effects take time to materialise. A recommendation model may appear to underperform in the short term while building stronger habits and lower churn over a longer horizon. Choose the right measurement window and be explicit about what you are measuring.
Key metrics to track:
- CTR (Click-Through Rate) on recommendations: How often do users click on what is shown?
- Completion rate: Is the recommended content actually finished?
- Return rate: Does the user return to the platform more frequently?
- Satisfaction signals: Rating data if the platform surfaces it.
- Churn delta: Does the churn rate differ between the test group and the control group?
Be mindful of Goodhart's Law: if you optimise a model to maximise CTR, it will recommend clickable titles, not necessarily good ones. Balance short-term engagement metrics with longer-horizon indicators that reflect genuine user satisfaction and retention.
Another trap is that recommendation models can create filter bubbles - users become locked in narrow content segments and are never exposed to breadth. Consider deliberately including an element of exploration in recommendations, for example ensuring that a defined share of suggestions consistently falls outside the user's established patterns.
Summary and Next Steps
A well-functioning recommendation system is not a single decision - it is an ongoing engineering project. It requires the right algorithms, clean and rich content metadata, a stable data infrastructure to collect and process user signals in real time, and a disciplined testing framework that supports data-driven decisions.
Do not start by building the most sophisticated system you can imagine. Start by solving the cold-start problem, establishing basic hybrid recommendations, and setting up a testing workflow. Iterate from there.
Shapp has built recommendation solutions for streaming platforms at various stages - from MVP to large-scale systems serving millions of users. If you want to discuss what fits your platform, take a look at what we do in AI development and streaming, or contact us directly for a no-obligation conversation.