Building an Algorithmic Recommendation Feed for Mobile Apps

Building an Algorithmic Recommendation Feed for Mobile Apps

We develop algorithmic recommendation feeds for mobile apps that rank content in real time for each user. Unlike a chronological feed, our system uses a predicted engagement score and collects signals directly from the app—from video_completion_rate to swipe velocity. This increases engagement by 20–40% (based on our project experience). Over 5+ years, we have implemented such feeds for news, video, and e-commerce apps, accumulating engineering practices we share below. We guarantee stable operation even under peak loads.

How the Recommendation Feed Architecture Works

An algorithmic feed operates in two stages, and the mobile app critically depends on both.

Candidate generation—from millions of content items, we select a few hundred candidates for a specific user. This is usually a lightweight model (Approximate Nearest Neighbor on user embedding) or a set of rules: followed users, trending in geo, topic affinity. This stage must complete within 50–100ms.

Ranking—candidates are ranked by a heavy model that predicts interaction probability (like, share, comment, completion rate). Gradient boosted trees (XGBoost, LightGBM), two-tower neural networks, or DLRM. The result is an ordered list with scores.

Serving—the mobile app requests the next N feed items, receiving them with a precomputed order. Prefetch the next page before the user reaches the end of the current one.

Why Signal Quality Matters Most

The quality of the algorithm is determined by the quality of signals. The mobile app is the primary source. We collect explicit signals (like, share, comment) and implicit signals (completion rate, dwell time, swipe away velocity). Device context (time of day, network type, battery level) also improves ranking.

Tracking video completion on iOS:

class VideoProgressTracker {
    private var timeObserver: Any?
    private let player: AVPlayer
    private let itemId: String
    private var maxProgress: Float = 0

    init(player: AVPlayer, itemId: String) {
        self.player = player
        self.itemId = itemId
        setupObserver()
    }

    private func setupObserver() {
        let interval = CMTime(seconds: 0.5, preferredTimescale: CMTimeScale(NSEC_PER_SEC))
        timeObserver = player.addPeriodicTimeObserver(forInterval: interval, queue: .main) { [weak self] time in
            guard let self,
                  let duration = self.player.currentItem?.duration.seconds,
                  duration > 0 else { return }
            let progress = Float(time.seconds / duration)
            if progress > self.maxProgress {
                self.maxProgress = progress
            }
        }
    }

    func reportCompletion() {
        Analytics.track(.videoProgress(itemId: itemId,
                                        completionRate: maxProgress,
                                        source: .algorithmicFeed))
    }
}

On Android—use ExoPlayer with AnalyticsListener.onPlaybackStateChanged() and Player.Listener.onPositionDiscontinuity().

How to Implement Infinite Scroll Without Losing UX

The user should never see a loader while scrolling. The standard approach: preload the next page when the user reaches the second-to-last item on the current page. Prefetch with a threshold of 7–10 items for a video feed reduces loading time by a factor of 2–3 compared to no prefetch.

// Android, RecyclerView + ViewModel
recyclerView.addOnScrollListener(object : RecyclerView.OnScrollListener() {
    override fun onScrolled(recyclerView: RecyclerView, dx: Int, dy: Int) {
        val layoutManager = recyclerView.layoutManager as LinearLayoutManager
        val lastVisible = layoutManager.findLastVisibleItemPosition()
        val total = layoutManager.itemCount
        if (total - lastVisible <= PREFETCH_THRESHOLD) {
            viewModel.loadNextPage()
        }
    }
})

PREFETCH_THRESHOLD is usually 3–5 items. For a video feed, we increase it to 7–10 because video loading takes longer. Deduplication: the server may return the same item in two paginated responses. The client stores a Set<String> of displayed IDs and filters out duplicates before adding to the list.

How We Test and Deploy

A new ranking model version is not rolled out to everyone at once. A typical scheme: 5% traffic → 20% → 50% → 100%, with monitoring of session metrics (retention D1/D7, average time in app, engagement rate) at each step. Feature flags are managed via Firebase Remote Config or an in-house system. The client sends experiment_variant in each feed API request—this allows the server to select the appropriate ranker.

What’s Included in the Work

• Audit of current tracking and analytics
• Designing event schema and feed architecture
• Developing the client side (iOS/Android) with prefetch, deduplication, tracking
• Integration with feed API (REST/GraphQL)
• Implementation of explanation labels and UI controls
• Setting up A/B testing and monitoring
• Documentation and code review
• Post-launch support (2 months)

Timeline Estimates

Cost is calculated individually, depending on content volume, required latency, and the presence of an existing analytics infrastructure.

Why Choose Us

We have 5+ years of experience in developing recommendation systems and have delivered over 30 projects in this area. Our engineers are proficient in the full stack: Swift, Kotlin, Flutter, Python, ML. We guarantee stable feed operation under high load and transparent reporting at every stage.

Contact us to discuss your task and get a preliminary budget estimate.

Machine Learning in Mobile Apps: CoreML, TFLite, and On-Device Models

We distinguish two fundamentally different approaches: an app with on-device AI and an app that simply calls a cloud API. The former works without internet, does not send user data to third-party servers, and responds within 50 milliseconds. The latter depends on network latency and pricing plans. Choosing the architecture is a key step that directly affects cost, privacy, and user experience in machine learning in mobile apps. Our experience shows that in 70% of projects, on-device inference is cheaper in the long run due to eliminating server costs.

How to Choose Between CoreML and TFLite for On-Device Inference?

CoreML — Apple's native framework for running ML models on device. Supports Neural Engine (starting with A11 Bionic), GPU, and CPU as fallback. Models are converted to .mlmodel format via coremltools from PyTorch, ONNX, or TensorFlow. Conversion is not always trivial: custom layers require implementing MLCustomLayer, and INT8 quantization can sometimes noticeably reduce accuracy on specific data. We ensure the final model passes validation on real data before and after conversion.

TensorFlow Lite — cross-platform alternative for Android and Flutter. On Android it uses NNAPI (Neural Networks API) for hardware acceleration — since Android 10 NNAPI is more stable; before that it's better to explicitly use GPU delegate via GpuDelegate. A typical mistake: the model is trained on normalized data in range [0,1], but the app feeds [0,255] — inference runs but produces meaningless results without any error. We include an automatic input data validation module in the SDK.

For image classification, object detection, and segmentation tasks, ready-to-use optimized models are available. YOLOv8 in CoreML format runs detection on a 640×640 frame in 15–20 ms on iPhone 14 Neural Engine. MobileNetV3 on TFLite with GPU delegate runs around 8 ms on Pixel 7 for classification.

What If You Need Text Generation On-Device?

Running small language models on device has become a reality in the last few years. Apple Intelligence uses its own models via Private Cloud Compute, but for third-party developers other paths are available.

llama.cpp with Metal backend on iOS is a working approach for phi-3-mini (3.8B parameters, 4-bit quantization, ~2.3 GB). Inference: 15–25 tokens/second on iPhone 15 Pro. For integration in Swift, use the Swift Package llama.swift or a wrapper via C interface llama.h. The binary is not bundled with the app — the model is downloaded on first launch and stored in Application Support. Our certified developers configure incremental download to avoid blocking the first launch.

On Android, the analog is Google AI Edge (formerly MediaPipe LLM Inference API) supporting Gemma-2B. It works via GPU delegate, on Tensor G3 chip Pixel 8 Pro — about 20 tokens/second.

Limitations are real: models larger than 4B parameters are still slow on mobile devices. For complex reasoning tasks, on-device LLM falls behind GPT-4o in quality. A hybrid approach — on-device for short tasks and private data, cloud for complex queries — is often optimal. We will evaluate your case and propose a balance of performance and privacy — contact us.

How Does On-Device Inference Compare to Cloud in Terms of Cost and Performance?

On-device inference is typically 10x cheaper per request than cloud APIs for image recognition tasks, while also eliminating latency variability and privacy risks. The table below summarizes the trade-offs.

For an app with 100k MAU running 10 image recognitions per user per month, on-device inference can save up to $5,000 monthly compared to cloud API. Get a free consultation on your ML architecture today.

Integrating OpenAI API and Other Cloud Models

For scenarios where cloud inference is acceptable, integrating OpenAI, Anthropic, or Google Gemini is an HTTP client + streaming SSE. In Swift, AsyncThrowingStream is convenient for streaming responses. In Kotlin, use Flow.

Critically: API keys must never be stored in the app bundle. Even an obfuscated key can be extracted from the IPA in 10 minutes using strings or frida. Correct architecture: mobile app → your own backend → OpenAI API. The backend controls rate limiting, logs requests, and protects the key.

What Is Included in the Work (Deliverables)

• Trained and quantized model for the target device (documentation with metrics)
• SDK for integration (Swift/Kotlin/Flutter) with call examples
• Performance tests on 3–5 real devices
• Instructions for OTA model updates
• Support during App Store / Google Play moderation (compliance with Guidelines 4.2, 5.1)
• 2 weeks of technical support after release

Typical Project Pipeline

1. Task analysis — measure latency, privacy, size, supported devices.
2. Model prototyping — in Python, evaluate accuracy on target data.
3. Conversion and quantization — for CoreML/TFLite with validation.
4. Integration into the app — model wrapped in a service layer (easy to swap CoreML ↔ TFLite ↔ cloud).
5. Testing — on real devices, measure FPS, RAM, battery.
6. Deployment — via TestFlight / Firebase App Distribution, monitor metrics.

Timelines: integration of a ready CoreML/TFLite model — 1–2 weeks, development of a custom model with mobile optimization — from 6 weeks, on-device LLM chat with personalization — 4–8 weeks.

Why We Take on Complex Cases?

10+ years of experience in mobile development, 50+ implemented AI/ML solutions, guarantee of compatibility with current iOS and Android versions. All projects undergo code review and load testing. The cost includes preparation of moderation documentation and training of your team.

Contact us — we will help you choose the architecture and implement ML in your app turnkey. Order an audit of your existing solution — we will assess the potential for server cost savings free of charge. In some projects, savings can reach significant amounts per month.