How to Build a Minimum Viable Recommender System (MVR) for a Media Platform
Take a quick glance around the web—recommender systems are everywhere, steering you toward new videos, articles, or that perfect pair of sneakers you didn’t know you needed. If you’re running a content platform or an e-commerce site without personalized recommendations, you might be leaving a substantial chunk of revenue—and user engagement—on the table. That said, most “How to build a recommender system” guides either go super deep into the latest machine learning techniques or skip the practical realities of building with limited time, data, and resources.
Let’s fix that.
In this article, I’ll share a practical approach to creating a Minimum Viable Recommender (MVR) — one that delivers real-world impact without needing a massive engineering force or a bottomless budget. If you work for a startup or a lean tech org, stick around. These 5 essentials will keep your recommender system lean, practical, and laser-focused on delivering real value:
Set Your North Star
Identify Your Power Signals
Choose a Lean Model
Build the Right Infrastructure
Validate and Iterate
Set Your North Star
It’s easy to get carried away by the shiny new AI model you’ve been itching to try. But remember: your recommender system is there to serve your business and product objectives, not the other way around. So, before you dive into code or data pipelines, figure out what you’re optimizing for and why.
Engagement: Maybe you want users to spend more time consuming content (session length), watch more videos, or scroll through more articles.
Growth: Perhaps you need to increase daily or monthly active users—or you want them to share your content with others, driving new signups.
Diversity & Discovery: You might also want to ensure new or niche creators get exposure, fostering a more inclusive content ecosystem.
It’s common for businesses to juggle multiple goals at once. For instance, a news site might want to surface the latest breaking news (in this case you need real-time updates) while also showcasing evergreen content. Decide which metrics matter most. Session length? Monthly active users? Click-Through-Rate? Each metric will shape how you design and tune your system.
Your goals are your compass. If your recommendation setup isn’t aligned with your business strategy, you’ll end up with an irrelevant system—or worse, a system that optimizes the “wrong” outcomes (like maximizing clicks at the expense of user satisfaction). So, define those objectives first, then keep them front and center.
Identify Your Power Signals
Once you know your targets, the next question is: What data do we have, and how can it support our business goals? Data is the fuel for your recommender—but in real scenarios, you rarely have it all. For instance, during my time on The New York Times personalization team, we didn’t record which articles a user actually saw in the mobile app, so we had to improvise with the signals we did have—like clicks and reads. The good news is, you don’t need every possible data point from day one.
What matters is matching each signal to the objective you’re trying to achieve. For example, clicks can measure immediate interest but might not reveal how deeply a user engages; time spent or scroll depth can better reflect genuine attention or curiosity; and likes, shares, or saves can indicate stronger commitment to your content, although they might be quite sparse.
Each type of data sheds light on a different facet of user behavior, so focus on the dense, high-quality signals that align with your end goals—whether that’s boosting session length, exposing new content creators, or driving higher conversion rates. Over time, as you gather more robust and varied data, you can refine your initial features or replace them with richer signals to further improve your system’s performance.
Choose a Lean Model
You might be tempted to jump straight into model-building. Afterall, it’s fun to train and test different models. But remember: the best model for your needs depends entirely on your goals (Step 1) and the data you’ve gathered (Step 2). There’s no one-size-fits-all solution, and you’ll likely want a simple approach to start—something you can iterate on as you learn more about your users.
More Than Just Collaborative vs. Content-Based
When most people think of recommender systems, they imagine collaborative filtering (users who like similar items) or content-based methods (items that share similar attributes). Those are solid foundations, but they’re not your only options. You can also leverage embeddings to represent items (and even users) in a vector space and then measure similarity between items (or users) via cosine distance. This can be surprisingly powerful for text or video content, especially if you tap into a pretrained model.
Embeddings vs. Reinforcement Learning
If your primary goal is to suggest relevant items based on immediate user preferences, embedding-based similarity might be all you need for your MVR. On the other hand, if you want to strike a balance between showing users what they already like and nudging them to discover new content, consider a reinforcement learning (RL) approach. RL excels at handling the classic “explore vs. exploit” dilemma, ensuring users don’t get stuck in a filter bubble while still receiving personalized options. If you’re curious to dip your toes in RL, starting with Thompson Sampling is a great way to learn the basics in a recommender context.
Recall & Ranking
Many production systems split the recommendation process into two stages:
Recall: Narrow down a large pool of candidates to those most likely to be relevant to the user.
Ranking: Use a more precise model—or logic—to reorder those candidates for the final top-K recommendations.
For smaller catalogs (under 10,000 items), you might skip the recall step and just rank everything. But as your candidate pool grows, separating recall and ranking becomes essential for efficiency. It also gives you room to experiment with different recall methods or specialized ranking models without overhauling your entire pipeline.
Ultimately, start with the method you can implement quickly and maintain easily. Given the number of libraries and pretrained models that are readily available, you could start with techniques that a while back were considered “sophisticated”—adding embeddings, introducing RL— for your MVR. Even a “lean” system now can leverage powerful models. Overtime, you can combine more methods or a hybrid model, or fine-tune your ranking logic, as your user base and data maturity expand.
Build the Right Infrastructure
One might argue that how you build your infrastructure should happen alongside choosing a model, but remember: infrastructure should support your model, not the other way around. The choices you make for databases, inference platforms, and data pipelines will shape how reliably—and how fast—your system can deliver recommendations at scale.
Your infrastructure choices don’t just affect uptime and latency; they also influence what recommendations users see. If data is only refreshed every 24 hours, you might miss opportunities to push trending content or react to sudden changes in user behavior. On the other hand, a real-time system could require hefty resources but allow you to serve ultra-fresh suggestions that align with evolving user interests or fast-breaking events.
For many new systems, a simple offline setup (batch updates once a day) is perfectly adequate—cheaper, easier to maintain, and often fresh enough for most use cases. If your product depends on real-time updates (think breaking news or fast-moving trends), you’ll need a more complex, costlier online setup with real-time data ingestion and frequent retraining, or even real-time training. That extra complexity can pay off in timelier suggestions but requires a bigger team and budget.
Focus on what your user base truly needs - if that is real-time recommendations then invest in this setup and not as much on a sophisticated model. If fresh recommendations are not critical for your product’s success, start lean while you’re still proving the value of personalization and scale up later knowing that the return on investment justifies the additional complexity.
Validate and Iterate
The final step is to confirm your recommender is actually doing what it’s supposed to do—and to keep improving it. That means offline model evaluation before letting real users near it, end-to-end checks so you’re not caught off guard by performance hiccups at scale, and A/B testing once it’s polished
Offline Model Evaluation
Testing your model offline is critical for rapid iteration, so don’t skip it. You simulate real-world conditions by loading your trained model as you would in production. Then, from a pool of candidate items available at a certain point in time, you try to predict which ones users actually engaged with. Common metrics include precision (how often your top recommendations are correct) and recall (how many relevant items you caught), but you can also measure diversity (are you suggesting the same item to everyone?) or freshness (how quickly do you reflect new content?). Catching logical or data issues offline saves you days—maybe weeks—compared to running flawed models in a live environment.
End-to-End Checks
Beyond measuring model performance, run end-to-end tests to ensure the entire recommender system works under real conditions. How quickly do you update user data? Are your recommendations going stale across users because the model isn’t retrained often enough? Does latency spike with more traffic? Addressing these operational bottlenecks before fully rolling out your recommender can spare you plenty of headaches down the road.
A/B Testing
Once your MVR shows promise offline, then it’s time to validate with real users. Split them into two groups:
Control (A): Sees your current setup or non-personalized recommendations.
Test (B): Gets your new MVR approach.
Measure the difference in key performance indicators—like session length, watch time, click-through-rate, or monthly active users. This is where you find out if your system actually does what you hoped. If it delivers the improvements you predicted, great. If not, you’ve got actionable insights about what to tweak.
Wrapping Up
A recommender system might look intimidating at first, but you don’t need a massive budget or a PhD in machine learning to get started. Building a minimum viable recommender system is more about clarity and practicality than advanced math. If you start with clear goals that align with your business strategy, pick the most impactful data you can realistically collect, choose a model that’s easy to deploy, set up lean infrastructure that scales with your needs, and evaluate thoroughly both offline and online, you’ll have a solid foundation for personalization.
By creating an MVR that zeroes in on your key objectives and resonates with your users, you set the stage for bigger leaps in personalization down the road for your product. So go forth, build that starter system, and see how it impacts your bottom line.
