Collaborative filtering

Collaborative filtering is a form of wisdom-of-the-crowd approach, where the set of preferences of many users with respect to items is used to generate estimated preferences of users for items with which they have not yet rated/reviewed. It works on the notion of similarity. Collaborative filtering is a methodology in which similar users and their ratings are determined not by similar age and so on, but by similar preferences exhibited by users, such as similar movies watched, rated, and so on.

Advantages of collaborative filtering over content-based filtering

Collaborative filtering provides many advantages over content-based filtering. A few of them are as follows:

• Not required to understand item content: The content of the items does not necessarily tell the whole story, such as movie type/genre, and so on.
• No item cold-start problem: Even when no information on an item is available, we still can predict the item rating without waiting for a user to purchase it.
• Captures...