Recommender Systems Definition

A recommender system (RecSys) is an information filtering system that suggests relevant items such as products, content, or services to users based on their preferences, behavior, past interactions or general information such as popularity. Recommender systems use algorithms, often but not always based on machine learning, to predict what users might like, without explicit user queries. Commonly employed in e-commerce, streaming platforms, and social media, recommender systems aim to personalize the user experience by surfacing content that aligns with individual interests.

The key difference between a recommender system and a search engine lies in how they operate:

• Recommender systems provide personalized suggestions proactively, often based on implicit signals (user behavior, preferences, ratings) without requiring a specific query from the user.
• Search engines, on the other hand, retrieve information in response to explicit user queries. They rely on the user’s input to display relevant results, typically focusing on matching keywords or phrases from the query.

In short, recommender systems aim to discover content you might not be actively looking for, while search engines help you find content you’re explicitly seeking.