generate title from the following introduction: Today, the information available on the Internet is large and uncontrolled for the masses of users. Users need only the necessary information for their purposes from Internet documents. For doing this it needs more specialized systems rather than traditional Information Retrieval systems to provide only necessary and interesting information to users. The best solution proposed for this problem is the recommendation system (RS). A recommendation system is a system which depends on the history of users and the information or documents accessed by users to make future recommendations. Users who want news information should only get information available from news sources [1], [2], [3]. The recommendation system uses basically the contents of the items and the user’s profile to provide the information. Therefore, the items must be known to the system, and user information is quite important in predicting user interest. Then the system will compute the relation of the items to be provided by the user to the user’s history. And, it is possible to provide similar items for similar groups of users by predicting the group’s similarity depending on the user’s profile history.
Many works have been done previously focussing on other similar areas like recommending popular news, fresh news, and topic-based news. Still, there is a problem in providing relevant information to new readers or to new users who do not have a history of data in the news recommendation system. The cold start problem of new users is a common problem in all recommendation systems due to the lack of information about new users in the system [10]. The lack of available user data in the situation where new users/ users join the system limits the effectiveness of the news recommender system to suggest relevant news to this kind of user. Due to this insufficient user data, there is an occurrence of a new user cold-start problem. This user data is divided into two, explicit data which is collected by the reader’s direct activity (i.e., when users give feedback directly to the news articles while she/ he is reading) and implicit data which the system collects by following the reader’s activities (i.e., using the reader’s link navigation behaviour including the time spent to read articles) in the system. These user data are not sufficient in order to suggest and recommend relevant news articles to the readers in the scenario where a new user joins the system [1], [3], [4]. Therefore, we proposed to use demographic data of the user for the new user to start the system.
The aim of this work is to recommend news for any reader even for a user who has no history in the recommendation system. The work is used to suggest news to online readers according to their interest of news from available online newspapers. Thus, rather than searching for all newspapers and news, it is good for the system to recommend only the interesting and related news to readers, depending on the user profile history, which is possible with the News Recommender system (NRS). This system will provide only the relevant information for the readers of news depending on their interest of news categories. For example, some readers need only a specific field of news that is more related to their profession. Whereas, others users want to read a more general field (i.e., sports or entertainment). This news recommendation system is used to help the users on their interest of news and to provide updated news for the users based on their interests. 
Our proposed system will use the hybrid of Content-based Filtering (CBF) and Collaborative Filtering (CF) approaches with user demographic data to predict the recommended news for readers. The new user data available that is used for the proposed system are the only demographic data that can be taken when registering for the website. As our proposed system is to fix the problem of new user interest of news, the system will fix it and recommend news for the user according to user groups and their patterns of interest. In addition, it recommends popular news by filtering the articles most read by many users and obtaining the highest rate value. And the user should recommend only the categories that s/he is more interested in. For example, if he/she is interested in entertainment news, he/she should have to get from this category and the same for other news categories. The system returns the news to all new users and existing users, which is category-based (i.e., the news to be recommended/predicted will be the category of news that users want to read). 
In this work, we investigated and examined three research questions. The first one is, how to model user demographics with a hybrid approach. The second one is how to figure out this impact on the new recommender scheme towards performance metrics such as Precision, Recall, and F1-Score. The last one is, how to model a new recommender system for a new user.