Netflix estimates that its recommendation engine saves the company over a billion dollars annually by reducing subscriber churn through content personalisation. Amazon attributes thirty-five percent of its revenue to recommendation-driven product discovery. For any platform that presents more content than users can browse, recommendations are not a feature — they are a core business function.
Recommendation systems work by predicting what a user will want based on their past behaviour, the behaviour of similar users, and the characteristics of available items. Three algorithmic approaches dominate production deployments.
Collaborative filtering is the classic approach: find users with similar taste profiles and recommend what they liked that the target user has not seen. It requires no item metadata — just interaction data — and discovers non-obvious correlations. Its weakness is the cold start problem: new users have no interaction history, and new items have no interaction data to inform their placement.
Content-based filtering uses item features — genre, author, topic, price range — to recommend items similar to those a user has previously engaged with. It handles cold start better but is limited by the richness of item metadata and the assumption that users want more of the same.
Hybrid approaches combine collaborative and content-based signals, typically using a two-stage architecture: a candidate generation layer that quickly retrieves hundreds of potentially relevant items from a large catalogue, followed by a ranking layer that uses a richer feature set to order the candidates for presentation.
For most Indian product companies building their first recommendation system, the practical advice is to start with existing interaction data and implement a collaborative filtering baseline using a matrix factorisation model. This delivers most of the business value with a fraction of the engineering complexity of a full two-stage architecture. Add complexity as you measure the incremental benefit.