AI Product Recommendation (Collaborative Filtering) for Mobile App

AI-Powered Collaborative Filtering Recommendations for Mobile Apps

Collaborative Filtering builds on one idea: if two users rated items similarly in past, their future preferences will match. No information about items needed — only interaction matrix "user × item". Why CF works where Content-Based fails: recommending films, music, content with unstructured metadata.

Technical Essence and Main Challenges

Matrix Factorization as Foundation

Classical CF via dot-product nearest neighbor search doesn't scale beyond 100K users. Working approach — decompose interaction matrix into two embedding spaces: users and items represented as fixed-dimension vectors (usually 64–256). Recommendation is finding items whose embeddings are closest to user's embedding.

Libraries for training: Implicit (Python, specialized for implicit feedback), LightFM (hybrid CF+content), RecBole (research framework with 70+ algorithms). For production deploy, usually Implicit + FAISS for ANN search.

Cold Start — CF's Main Problem

New user without interaction history — typical cold start. Standard solution: first 5–10 interactions use "popular items from interest category" rule (onboarding with preference selection). After accumulating minimal history, switch to personalized model.

In code this looks like strategy pattern:

// Android: recommendation strategy based on history
interface RecommendationStrategy {
    suspend fun getRecommendations(userId: String, count: Int): List<Product>
}

class ColdStartStrategy(private val api: RecommendationApi) : RecommendationStrategy {
    override suspend fun getRecommendations(userId: String, count: Int) =
        api.getPopularByPreferences(userId, count)
}

class CFStrategy(private val api: RecommendationApi) : RecommendationStrategy {
    override suspend fun getRecommendations(userId: String, count: Int) =
        api.getPersonalized(userId, count)
}

class RecommendationRepository(private val api: RecommendationApi) {
    suspend fun getRecommendations(user: User): List<Product> {
        val strategy = if (user.interactionCount < 10) {
            ColdStartStrategy(api)
        } else {
            CFStrategy(api)
        }
        return strategy.getRecommendations(user.id, count = 20)
    }
}

Implicit vs Explicit Feedback

In most mobile apps users don't explicitly rate items. Implicit feedback — views, clicks, add to cart, card view time — much more informative but needs proper weighting: view without click ≠ interest, click without purchase ≠ satisfaction.

Practical weighting scheme:

Client Event Logging

CF quality directly depends on logging completeness and accuracy:

// iOS: track interactions with view duration precision
class ProductInteractionTracker {
    private var viewStartTime: Date?
    private let analytics: AnalyticsService

    func trackViewStart(productId: String) {
        viewStartTime = Date()
    }

    func trackViewEnd(productId: String) {
        guard let start = viewStartTime else { return }
        let duration = Date().timeIntervalSince(start)

        if duration > 3.0 {
            analytics.log(InteractionEvent(
                productId: productId,
                type: .view,
                weight: min(Int(duration / 3), 3),  // cap at weight 3
                timestamp: start
            ))
        }
        viewStartTime = nil
    }

    func trackAddToCart(productId: String) {
        analytics.log(InteractionEvent(productId: productId, type: .addToCart, weight: 7))
    }
}

View time tracking via viewStartTime distinguishes accidental views from genuine interest. Without this signal, interaction matrix gets noisy from random data.

Serving: FAISS for ANN Embedding Search

Trained model exports item embeddings to FAISS index. On recommendation request for user: get user embedding → find K nearest items in FAISS → filter already bought → return list. Latency with 1M items — 5–15 ms on server.

Process

Data audit: interaction matrix volume, sparsity, cold start problem presence.

Event logging setup on iOS/Android clients.

Model training (Implicit ALS or LightFM) on historical data.

Recommendation API development + FAISS serving.

A/B test: CF recommendations vs popular items → measure CTR and conversion.

Timeline Guidance

MVP with Implicit ALS + basic serving — 2–3 weeks. Full system with event logging, cold start fallback, A/B testing, monitoring — 6–8 weeks.