Personalization engine

Recommender Systems

A hybrid recommendation engine combining collaborative filtering and content-based approaches to deliver personalised suggestions that balance relevance and novelty.

Collaborative filteringSVDTF-IDFHybrid systems

Engagement: Applied AI delivery
System type: Personalization engine
Primary outcome: Operational leverage

Case architecture09

Challenge

The existing operating model made personalization engine slow to inspect, difficult to scale and dependent on manual coordination.

Response

Collaborative filtering via matrix factorization (SVD) on behavioural data. Content-based layer (TF-IDF, embeddings) for cold-start coverage

Result

Increases engagement and conversion — improving click-through, session depth and overall platform stickiness.

System architecture

From signal to controlled action.

Collaborative filtering via matrix factorization (SVD) on behavioural data

Content-based layer (TF-IDF, embeddings) for cold-start coverage

Hybrid scoring and ranking with configurable weighting

Business value

The system changes the operating baseline.

Increases engagement and conversion — improving click-through, session depth and overall platform stickiness.

Next case: Robot Movement Consulting

From signal to controlled action.

The system changes the operating baseline.

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