Different types of recommender systems

In this section, we’ll delve into the diverse types of recommendation engines, shedding light on their methodologies and the unique advantages each brings to the table:

• Collaborative filtering:

  This approach is based on the idea that users who have agreed in the past will agree in the future about their preference for certain items. As shown in the following figure, the model tries to find similar users by looking at their viewing/reading and recommends the content viewed by one user to other, similar users:

Figure 15.1 – Collaborative filtering

• Content-based filtering:

  This method uses item attributes to recommend additional items similar to what the user likes, based on their previous actions or explicit feedback.

  For example, if a user has shown a preference for movies directed by Christopher Nolan, the system will rank the movies that were directed by him higher when making recommendations...