Product recommendations have come a long way from simple "customers who bought X also bought Y." Modern AI models use embeddings, sequence models, and reinforcement learning to surface the right products at the right time.
Beyond Collaborative Filtering
Collaborative filtering relies on past behaviour. New approaches combine content features (product attributes, images) with behavioural signals and contextual data (time, device, session) for more accurate recommendations.
Key Techniques
- Embeddings: Represent products and users in a shared vector space for similarity search.
- Sequence models: Use browsing and purchase history to predict next-best action.
- Multi-armed bandits: Balance exploration and exploitation for personalised ranking.
Measuring Impact
Track click-through rate (CTR), add-to-cart rate, and revenue attributed to recommendations. A/B test model updates and ranking changes. Avoid optimising for clicks at the expense of conversion.
Need help implementing AI-driven recommendations? Get in touch with our team.