Recommender systems are fundamental solutions to information overload on the web due to the availability of multitude information sources. This system filters and presents relevant information to customer and/ or online users, a small subset of items that she/he is most likely to be interested in. The application of Recommendation schemes ranges from entertainment application (i.e., movies, music) to online newspapers information sources to recommend for the users based on their preferences. News recommendation scheme utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of existing news recommendation scheme is limited during a scenario where information about a user or information about set of users in the system is unavailable. This leads to the occurrence of new user cold start problem. Therefore, the main objective of the study is: designing news recommender system using hybrid approaches to address new user cold start problem to ease and suggest more related news article for new users. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a news articles dataset with user rating value and user demographic data. The performance of the proposed model was evaluated using precision, Recall and F1-Score metrics which support the effectiveness of the proposed model. The proposed model performance is done by two ways of experiment. The performance of the proposed model performs around 78.04% of Precision, 78.71% of Recall and 78.37% of the average F1_score for the experiment based on individual user similarity in the system. And also performs around 86.11% of precision, 88.41% of recall and 87.25% F1-score for the similarity of users based on the similarity of users within the same cluster which is better than the first experiment. The proposed model achieved an efficient performance results towards Precision, Recall and F1-Score of information accuracy metrics. Keywords: News Recommendation System, Clustering, Cold start Problem, Hybrid Approach, Demographic information, New Users, Popular News.