Emotional news recommender system

With rapid advances of internet and overloading of information, it is important that we use some models and techniques to help users find proper data among massive flooding of information, especially in news domain that rapidly change. Recommender systems are a great help for achieving this goal. The current news recommender systems have focused on learning what users like to read based on their past activities and using methods for recommending news in a real-time manner, but none of them have considered emotion of news and how a user feels about an article in their recommendation process. Positive news can have a positive impact on user´s mood. In this work we aim to introduce a model for news recommender systems that can recommend news in a way to have a positive impact on the user´s mood. It utilizes both emotion of news and the user´s preference.