Recommender systems typically produce recommendations using one or more of the three approaches: content-based, collaborative filtering, or hybrid systems.
Content-based filtering recommender systems analyze items (music, movies, articles, products, touristic attractions, etc.) to understand the characteristics of those items and recommend similar or recommended items to users. This approach is generally used when there is sufficient data available about the items in question.
Collaborative filtering recommender systems, on the other hand, produce recommendations based on the collective behavior of a group of users and user similarity. This approach is generally used when there is insufficient explicit data about the items in question and relies on the "wisdom of the crowd" to make recommendations based on similar users.
Hybrid recommender systems use a combination of content-based and collaborative filtering approaches to make recommendations. These systems help improve recommendations from inadequate, insufficient, and infrequent datasets or are applied in solutions where both approaches matter to provide the highest level of personalization.