Recommender systems aim to assist users in discovering relevant items from a large pool of options
They focus on tailoring recommendations to individual users, taking into account their preferences, interests, and behavior.
Recommender systems rely on user data to generate recommendations.
One common approach is collaborative filtering. It looks for patterns and connections among users.
Another approach is content-based filtering, which recommends items based on the similarity of their attributes or content
Many recommender systems combine collaborative filtering and content-based filtering to improve recommendation accuracy.
Recommender systems are widely used in various industries, such as e-commerce, entertainment, social media, and news platforms.
Some recommender systems operate in real-time, continuously adapting to user preferences and providing instant recommendations.