How to Build a Recommendation System

Collaborative filtering content-based two-tower - the recommendation architecture that drives real engagement.

Recommendation systems are one of the highest-ROI ML investments for consumer products. They are also heavily over-engineered at early stage. This guide covers the right algorithm for your scale and data maturity - from simple popularity-based recommendations to production two-tower neural retrieval.

No fluff. Production-grade answers from engineers who ship AI into real products.

Start Simple: The Recommendation Algorithm for Your Data Maturity

Most teams reach for collaborative filtering before they have enough data for it to work. The right starting point depends on how much interaction data you have. Under 10k users: content-based filtering (item attributes TF-IDF similarity) or popularity-based. No interaction data needed. Simple to implement explainable. 10k-1M interactions: matrix factorization (ALS SVD) via implicit or Surprise library. 1M+ interactions: two-tower neural retrieval for candidate generation learning-to-rank for scoring.

At Valletta Software, we focus on:

Popularity baseline: trending items for new users - always have a fallback for cold start

Content-based: TF-IDF or embedding similarity on item attributes - no user data needed

Collaborative filtering: ALS matrix factorization with implicit library - for 10k+ user interactions

Two-tower model: user tower and item tower trained jointly - for 1M+ interactions with GPU

Candidate retrieval: ANN vector search over item embeddings - retrieve 100-1000 candidates

Reranking: LightGBM or XGBoost on rich features - score and reorder candidates

AB testing: measure CTR engagement time conversion - not just offline recall metrics

The Cold Start Problem and How to Handle It

Cold start is not a problem to solve once - it is an ongoing challenge for any growing product.

We give you more than just people. We give you top performers who drive results.

New user cold start: onboarding questions to gather preferences - explicit signals trump implicit

New item cold start: content-based similarity to existing items - items enter the catalog with recommendations

Exploration: epsilon-greedy or Thompson sampling - occasionally recommend outside the predicted preference

Contextual signals: time of day device type session context - improve recommendations without history

Transfer learning: pretrain on similar domain data - reduce cold start duration significantly

Hybrid approach: blend content-based and collaborative signals - content for new items CF for established

Fallback hierarchy: personalized then contextual then popular then random - never show nothing

Your team, your direction. We handle the rest. Rates from EUR 45/h.

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How to Build a Recommendation System - With Engineers Who Ship Them at Scale

Forget the hype. We make AI work in the real world.

Our engineers are trained in the latest AI tooling - Copilot, Claude Code, Cursor, LangChain, and vector databases - and use them daily to ship production AI features, not just prototypes.

Choose from a solo dev, mini team, or full squad. All powered by AI and ready to build from day one.

Lets keep it simple.

Our AI engineers build recommendation systems matched to your data maturity: content-based for early stage ALS collaborative filtering for growth stage two-tower neural retrieval for scale. All with AB testing infrastructure from day one.

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