Paper Title
A Hybrid News Recommender System

Recommender systems are built to help us to easily find the most proper information on the internet. Unlike the search engines recommender systems bring the information to the user without any manual search effort. This is achieved by using the similarities between users and/or items. There are many methods to build a recommender system and these methods can be applied to many specific domains like shopping, movies and music. Since each application domain has its own specific needs, the method used for recommendations differs. As a specific application domain, news recommender systems aim to give the most relevant news article recommendations to users according to their personal interests and preferences. News recommendation have specific challenges when compared to the other domains. From the technical point of view there are many different methods to build a recommender system. Thus, while general methods are used in news recommendation, researchers also need some new methods to make proper news recommendations. The proposed framework for building automatic recommendations in news is composed of two modules: an off-line module which preprocesses data to build reader and content models, and an online module which uses these models on-the-fly to recognize the reader’ needs and goals, and predict a recommendation list. The recommended objects are obtained by using a range of recommendation strategies based mainly on content based filtering and collaborative filtering approaches, each applied separately or in combination. Index terms- Recommender System, News Recommendation, Content-based, Collaborative Filtering, Hybrid System.