There are two main approaches you can take when it comes to filtering information.
Content-based filtering is an unsupervised mechanism based on the attributes of the items and the preferences and model of the user.
For example, if a user views a movie with a certain set of attributes, such as genre, actors, and awards, the systems recommend items with similar attributes. The preferences of the user (for example, previous "likes" for movies) are mapped with the attributes or features of the recommended item.
User ratings are not required in this approach. However, this approach requires considerable effort when it comes to feature or attribute extraction, and it is also relatively less precise than collaborative filtering approaches, which we will discuss later.