Oct 13, 2025·8 min
How to Create a Mobile App for AI-Based Recommendations
Learn how to plan, build, and launch a mobile app with AI-based recommendations—from data and UX to model choice, testing, and privacy best practices.
Learn how to plan, build, and launch a mobile app with AI-based recommendations—from data and UX to model choice, testing, and privacy best practices.
AI-based recommendations are app features that decide what to show next for each user—products, videos, articles, lessons, destinations, or even UI shortcuts—based on behavior and context.
Most recommendation experiences in mobile apps boil down to a few building blocks:
Recommendations should map to measurable outcomes. Typical metrics include CTR (tap-through rate), conversion (purchase/subscription), watch time/read time, and longer-term retention (day 7/day 30 return rates).
Pick one “north star” metric and add a couple of guardrails (e.g., bounce rate, refunds, churn, or feed load time) so you don’t accidentally optimize for clicks that don’t matter.
A recommendation engine is not a one-time feature. It usually starts simple and gets smarter as your app collects better signals (views, clicks, saves, purchases, skips) and learns from feedback over time.
Recommendations work best when they solve a specific “stuck moment” in your app—when users don’t know what to do next, or there are too many options to choose from.
Before thinking about models, choose the exact journey step where recommendations can remove friction and create a clear win for both users and the business.
Start with the path that drives most value (and has the most decision points). For example:
Look for high drop-off screens, long “time to first action,” or places where users repeatedly back out and try again.
To keep your MVP focused, pick one surface to start with and do it well:
A practical default for many apps is the product/detail page, because the current item is a strong signal even when you know nothing about the user.
Write these as one sentence each for your chosen surface:
This keeps you from building something that’s “accurate” in theory but doesn’t move outcomes.
Keep them specific and testable. Examples:
Once these are clear, you’ll have a concrete target for data collection, model choice, and evaluation.
Recommendations are only as good as the signals you feed them. Before you pick an algorithm, map what data you already have, what you can instrument quickly, and what you should avoid collecting.
Most apps start with a mix of “backend truth” and “app behavior.” Backend truth is reliable but sparse; app behavior is rich but requires tracking.
Treat “exposure” as first-class data: if you don’t record what was shown, it’s hard to evaluate bias, diagnose issues, or measure lift.
Start with a small, well-defined event set:
For each event, decide (and document): timestamp, item_id, source (search/feed/reco), position, and session_id.
Recommendations improve dramatically with clean item fields. Common starters include category, tags, price, length (e.g., read time/video duration), and difficulty (for learning/fitness).
Keep a single “item schema” shared by analytics and your catalog service, so the model and the app speak the same language.
Define identity early:
Make merge rules explicit (what to merge, how long to keep guest history), and document them so your metrics and training data stay consistent.
Good recommendations need data, but trust is what keeps users around. If people don’t understand what you collect (or feel surprised by it), personalization can quickly feel “creepy” instead of helpful.
The goal is simple: be clear, collect less, and protect what you keep.
Ask for permission at the moment it makes sense—right before a feature needs it—not all at first launch.
For example:
Keep consent wording plain: what you collect, why you collect it, and what the user gets in return. Provide a “Not now” path whenever the feature can still work (even if less personalized). Link to your Privacy Policy using a relative link like /privacy.
A recommendation engine rarely needs raw, sensitive detail. Start by defining the minimal signals required for your chosen use case:
Collect fewer event types, reduce precision (e.g., coarse location), and avoid storing unnecessary identifiers. This lowers risk, reduces compliance overhead, and often improves data quality by focusing on signals that actually help ranking.
Set a retention window for behavioral logs (for example, 30–180 days depending on your product) and document it internally. Make sure you can honor user-requested deletion: remove profile data, identifiers, and associated events used for personalization.
Practically, that means:
Be especially cautious with health data, data about children, and precise location. These categories often trigger stricter legal requirements and higher user expectations.
Even if it’s allowed, ask: do you truly need it for the recommendation experience? If yes, add stronger safeguards—explicit consent, stricter retention, limited access internally, and conservative defaults. For kids-focused apps, assume additional restrictions and consult legal guidance early.
A recommendation engine can be excellent and still feel “wrong” if the in-app experience is confusing or pushy. Your goal is to make recommendations easy to understand, easy to act on, and easy to correct—without turning the screen into a wall of suggestions.
Start with a few familiar modules that fit naturally into common mobile layouts:
Keep module titles specific (e.g., “Because you listened to Jazz Classics”) rather than generic (“Recommended”). Clear labels reduce the feeling that the app is guessing.
Personalization is not a license to add endless carousels. Limit the number of recommendation rows per screen (often 2–4 is enough for an MVP) and keep each row short. If you have more content, provide a single “See all” entry that opens a dedicated list page.
Also think about where recommendations fit best:
Recommendations improve faster when users can correct them. Build lightweight controls into the UI:
These controls aren’t just for UX—they generate high-quality feedback signals for your recommendation engine.
New users won’t have history, so plan an empty state that still feels personalized. Options include a short onboarding picker (topics, genres, goals), “Trending near you,” or editor’s picks.
Make the empty state explicit (“Tell us what you like to personalize your picks”) and keep it skippable. The first session should feel useful even with zero data.
You don’t need a complex model to start delivering useful recommendations. The right approach depends on your data volume, how fast your catalog changes, and how “personal” the experience must feel.
Rule-based recommendations work well when you have limited data or want tight editorial control.
Common simple options include:
Rules are also useful as fallbacks for the cold start problem.
Content-based recommendations match items similar to what a user already liked, based on item features such as category, tags, price range, ingredients, artist/genre, difficulty level, or embeddings from text/images.
It’s a strong fit when you have good metadata and want recommendations that remain meaningful even with fewer users. It can get repetitive without variety controls.
Collaborative filtering looks at user behavior (views, likes, saves, purchases, skips) and finds patterns like: “People who engaged with X also engaged with Y.”
This can surface surprising, high-performing suggestions, but it needs enough interactions to work well and can struggle with brand-new items.
Hybrid systems combine rules + content + collaborative signals. They’re especially useful when you need:
A common hybrid setup is to generate candidates from curated/popular lists, then re-rank with personalized signals where available.
Where your recommendation engine “lives” affects cost, speed, privacy posture, and iteration velocity.
Hosted recommendation APIs can be best for an MVP: faster setup, fewer moving parts, and built-in monitoring. The trade-off is less control over modeling details and sometimes higher long-term cost.
A custom recommendation service (your own backend) gives you full control over ranking logic, experimentation, and data usage. It usually requires more engineering: data infrastructure, model training, deployment, and ongoing maintenance.
If you’re early, a hybrid approach often works well: start with a simple custom service + rules, then add ML components as signals grow.
If your bottleneck is simply building the app surfaces and backend plumbing fast enough to start collecting signals, a vibe-coding platform like Koder.ai can help you prototype the recommendation UI and endpoints quickly from a chat-based workflow. Teams commonly use it to spin up a React-based web admin, a Go + PostgreSQL backend, and a Flutter mobile app, then iterate with snapshots/rollback as experiments evolve.
Most production setups include:
Server-side is the default: easier to update models, run A/B tests, and use larger compute. The downside is network dependency and privacy considerations.
On-device can reduce latency and keep some signals local, but model updates are harder, compute is limited, and experimentation/debugging is slower.
A practical middle ground is server-side ranking with small on-device UI behaviors (e.g., local re-ordering or “continue watching” tiles).
Set clear expectations early:
This keeps the experience stable while you iterate on quality.
A recommendation engine is only as good as the pipeline feeding it. The goal is a repeatable loop where app behavior becomes training data, which becomes a model, which improves the next set of recommendations.
A simple, reliable flow looks like:
App events (views, clicks, saves, purchases) → event collector/analytics SDK → backend ingestion (API or stream) → raw event store → processed training tables → model training job → model registry/versioning → serving API → app UI.
Keep the app’s role lightweight: send consistent events with timestamps, user IDs (or anonymous IDs), item IDs, and context (screen, position, referrer).
Before training, you’ll typically:
Also define what counts as a “positive” signal (click, add-to-cart) vs. exposure (impression).
Avoid random splits that let the model “peek” into the future. Use a time-based split: train on earlier events and validate on later events (often per user), so offline metrics better reflect real app behavior.
Start with a cadence you can sustain—weekly is common for MVPs; daily if inventory or trends change quickly.
Version everything: dataset snapshot, feature code, model parameters, and evaluation metrics. Treat each release like an app release so you can roll back if quality drops.
A recommendation model isn’t just “one algorithm.” Most successful apps combine a few simple ideas so results feel personal, varied, and timely.
A common pattern is two-stage recommendation:
This split keeps your app responsive while still allowing smarter ordering.
Embeddings turn users and items into points in a multi-dimensional space where “closer” means “more similar.”
In practice, embeddings often power candidate generation, and a ranking model refines the list using richer context (time of day, session intent, price range, recency, and business rules).
Cold start happens when you don’t have enough behavior data for a user or a new item. Reliable solutions include:
Even a strong ranker can over-focus on one theme. Add simple guardrails after ranking:
These guardrails make recommendations feel more human—useful, not monotonous.
Recommendation quality isn’t a feeling—you need numbers that show whether users are actually getting better suggestions. Measure in two places: offline (historical data) and online (in the live app).
Offline evaluation helps you compare models quickly using past interactions (clicks, purchases, saves). Common metrics include:
Offline scores are great for iteration, but they can miss real-world effects like novelty, timing, UI, and user intent.
Once recommendations are live, measure behavior in context:
Choose one primary metric (like conversion or retention) and keep supporting metrics as guardrails.
Without a baseline, “better” is guesswork. Your baseline might be most popular, recently viewed, editor picks, or simple rules.
A strong baseline makes improvements meaningful and protects you from shipping a complex model that performs worse than a basic approach.
Run controlled A/B tests: users randomly see control (baseline) vs. treatment (new recommender).
Add guardrails to catch harm early, such as bounce rate, complaints/support tickets, and revenue impact (including refunds or churn). Also watch performance metrics like feed load time—slow recommendations can quietly kill results.
Shipping recommendations isn’t just about model quality—it’s about making the experience fast, reliable, and safe under real traffic. A great model that loads slowly (or fails silently) will feel “broken” to users.
Aim for predictable scrolling and quick transitions:
Track the full chain from event collection to on-device rendering. At minimum, monitor:
Add alerting with clear owners and playbooks (what to roll back, what to disable, what to degrade to).
Give users explicit controls: thumbs up/down, “show less like this,” and “not interested.” Convert these into training signals and (when possible) immediate filters.
Plan for manipulation: spammy items, fake clicks, and bot traffic. Use rate limits, anomaly detection (suspicious click bursts), deduping, and downranking for low-quality or newly created items until they earn trust.
Shipping recommendations isn’t a single “go live” moment—it’s a controlled rollout plus a repeatable improvement loop. A clear roadmap keeps you from overfitting to early feedback or accidentally breaking the core app experience.
Start small, prove stability, then widen exposure:
Keep the old experience available as a control so you can compare outcomes and isolate the impact of recommendations.
Before increasing rollout percentage, confirm:
Run improvements in short cycles (weekly or biweekly) with a consistent rhythm:
If you want implementation details and rollout support options, see /pricing. For practical guides and patterns (analytics, A/B testing, and cold start), browse /blog.
If you’re trying to move quickly from “idea” to a working recommendation surface (feed/detail modules, event tracking endpoints, and a simple ranking service), Koder.ai can help you build and iterate faster with planning mode, deploy/host, and source code export—useful when you want the speed of a managed workflow without losing ownership of your codebase.
Start with one surface where users commonly get “stuck,” such as a product/detail page or search results. Write one user goal and one business goal (e.g., “help me compare quickly” vs. “increase add-to-cart rate”), then define 3–5 user stories you can test.
A focused MVP is easier to instrument, evaluate, and iterate than a broad “personalized home feed” on day one.
Most apps use a small set of interaction events:
view (detail opened, not just shown)impression/exposure (what recommendations were displayed)click (tap from a recommendation module)save / add_to_cartpurchase / subscribeskip / dismiss / quick bounceInclude consistent fields like user_id (or anonymous ID), item_id, timestamp, source (feed/search/reco), position, and session_id.
Log an exposure (impression) event whenever a recommendation module renders with a specific ordered list of item IDs.
Without exposure logging you can’t reliably compute CTR, detect position bias, audit what users were shown, or understand whether “no click” was because items were bad or because they were never displayed.
Pick one primary “north star” metric aligned to the surface (e.g., conversion on a shopping detail page, watch time on a media feed). Add 1–3 guardrails such as bounce rate, refunds/cancellations, complaint rate, or latency.
This prevents optimizing for easy wins (like CTR) that don’t improve real outcomes.
Use a layered fallback strategy:
Design the UI so empty states never show a blank screen—always show a safe default list.
Rules are best when you need speed, predictability, and a strong baseline (popularity, newest, curated lists). Content-based filtering works well when item metadata is strong and you want relevance with limited user interactions.
Collaborative filtering typically needs more behavior volume and struggles with brand-new items, so many teams adopt a hybrid: rules for coverage, ML for re-ranking when signals exist.
Build a hybrid system that combines:
This approach improves coverage, reduces repetitiveness, and gives reliable fallbacks when data is sparse.
Set clear product and engineering targets:
Use caching (per user/segment), return results in pages (10–20 items), and prefetch the first page so screens feel instant even on poor networks.
Use a time-based split: train on earlier interactions and validate on later ones. Avoid random splits that can leak future behavior into training.
Also define what counts as a positive (click, add-to-cart) vs. just an impression, and deduplicate/sessionize events so your labels reflect real user intent.
Collect only what you need, explain it clearly, and give users control:
Link policy details with a relative URL like /privacy and ensure deletions propagate to analytics, feature stores, and training datasets.