Recommendation systems are algorithms that provide personalized suggestions to users on digital platforms like e-commerce websites and social networks. They analyze large datasets to develop models of users' likes and interests, recommending similar items to individual users. These systems are ubiquitous, used by platforms like Netflix, YouTube, and Amazon to suggest content or products based on users' past interactions and preferences.
The main difference between Collaborative Filtering and Content-based Filtering lies in how recommendations are generated. Collaborative Filtering relies on the ratings or feedback of other users with similar preferences. It assumes that users who liked certain items in the past will like similar items in the future. In contrast, Content-based Filtering uses the features or attributes of the items themselves to recommend items similar to those the target user has interacted with before. It assumes that users who liked certain features of an item will like other items with similar features.