Recommendation Engines: Personalisation in Digital Products
Recommendation engines predict what content, products, or actions a user is most likely to find valuable — and present them proactively. They are one of the highest-impact features for e-commerce, media, and SaaS products, directly driving engagement, discovery, and revenue.
Types of Recommendation Approaches
- Collaborative filtering: "Users like you also liked..." — identifies users with similar behaviour patterns and recommends what they engaged with. Requires sufficient user data to be effective.
- Content-based filtering: "Because you liked X..." — recommends items similar in attributes to what the user has previously engaged with. Works well for new users with little history.
- Hybrid approaches: Combine collaborative and content-based signals — most production recommendation systems use hybrid approaches
- Popularity-based: Recommend what is most popular — simple, effective as a cold-start fallback
- Contextual: Incorporate context (time, location, device, session behaviour) into recommendations
Cold Start Problem
New users have no behaviour history — making personalised recommendations difficult. Solutions: ask users about preferences during onboarding, use demographic data, start with popularity-based recommendations and transition to personalised as behaviour accumulates.
Implementation Options
- Custom ML models: Maximum control, requires data science expertise and sufficient data volume
- AWS Personalize: Managed ML-based recommendation service — reduces implementation complexity
- Recommendation APIs: Algolia Recommend, Recombee — pre-built recommendation services