Hands-On Machine Learning with C++

Content-based recommendations

Personal recommendations use the maximum information available about the user—primarily, information on their previous purchases. Content-based filtering was one of the first approaches to this. In this approach, the product's description (content) is compared with the interests of the user, obtained from their previous assessments. The more the product meets these interests, the higher the potential interest of the user. The obvious requirement here is that all products in the catalog should have a description.

Historically, the subject of content-based recommendations was products with unstructured descriptions: films, books, or articles. Their features may be—for example—text descriptions, reviews, or casts. ...