What is the difference between collaborative filtering and content-based recommendation?

Content-based recommendation only considers the properties of the objects themselves, and forms a set of objects according to their labels. If you consume one of the collections, you will be recommended to other objects in the collection.

The recommendation algorithm based on collaborative filtering makes full use of collective wisdom, that is, collecting answers from a large number of people's behaviors and data to help us get statistical conclusions about the whole population. Recommendation is highly personalized, based on the following two starting points: (1) users with similar interests may be interested in the same thing; (2) Users may prefer products similar to those they have already purchased. That is to say, considering the historical habits of users, objectively, the objects are not necessarily similar, but subjectively, people's behaviors can be considered similar, so recommendations can be generated.