What are the two main types of filtering in machine learning? Explain.
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In machine learning, there are two main types of filtering: collaborative filtering and content-based filtering.
•Collaborative filtering is based on the idea that people who have agreed on similar items in the past are likely to agree on future items. Therefore, it is a process that uses information from a group of users to recommend items to other users. Collaborative filtering can be further divided into two types: memory-based and model-based. Memory-based collaborative filtering uses the entire set of user ratings to calculate similarities between users or items, while model-based collaborative filtering builds a model from the user data to make predictions about user preferences.
•Content-based filtering, on the other hand, focuses on the features of the items themselves. This type of filtering uses the attributes of an item to recommend other similar items to the user. Content-based filtering is particularly useful when there is a limited data set or when the item features are well-defined.
