Content-based filtering

Content-based filtering is based on creating a detailed model of the content from which recommendations are made, such as the text of books, attributes of movies, or information about music. The content model is generally represented as a vector space model. Some of the common models for transforming content into vector space models are TFIDF, the Bag-of-words model, Word2Vec, GloVe, and Item2Vec. 

Along with the content model, a user profile is also created using information about the user. Content is recommended based on matching the user profile with the content model.

Advantages of content-based filtering algorithms

The following are the advantages of content-based filtering algorithms:

• Eliminates the cold-start problem for new items: If we have enough information about the users, and detailed information about the new content, then the cold-start problem found in collaborative filtering algorithms does not affect content-based algorithms. The recommendation can be...