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It generates the suitable recommendations for a client that depends on his previous behaviors. It analyzes the user’s previous history like what liked, bought, or watched and accordingly it predicts. It generates a user profile for every user based on their previously selected items and recommends items to him based on similar features items which he liked before. It does not compare his preferences to the users to characterize each user. Content-based filtering approach is divided in three steps which are item representation, learning the user profile, and recommendations generator. In the item representation step, the information or the description of item is extracted to create item’s characteristics. It produces the structured item’s representation. In the next step, a user profile is generated. This user profile is based on the previous behavior such as liking or disliking, the rating or by writing some text comment given by the user for a particular item. This step is known as learning the user profile and the last step is recommendation generator. In this step, a list of recommended items is generated and compared it with the item’s features of the client’s profile. The item that is suitable or most likely is added to the prediction list [1].

This method has been executed in various domains like textual details such as websites, news, and articles and also used for recommending activities such as tourism, travel, TVs, and e-commerce industries. This method works very efficiently if the items size is moderate. This approach relies on content or characteristics of each item so it gives several advantages like it offers a high level personalization in recommendations; it can make its scale up when number of users increases, which means it is scalable. It can make recommendations with a particular interest of a user and it provided a very good security also. These are some advantages of content-based filtering approach [1].