Personalized Microblogs Corpus Recommendation Based on Dynamic Users Interests

Microblogs are specialized virtual social network web-based applications. Nowadays, following the microblogs is becoming more challenging as users can receive thousands of corpus updates every day. Going through all the corpuses updates is a time consuming process and affects the user´s productivity in real life, especially for the users who have a lot of followees and thousands of tweets arriving at their timelines everyday. In this paper, we propose a personalized recommendation system that aims at giving the user a summary of all received corpuses. Considering the fact that the user interests changes over time, this summary should be based on the user´s level of interest in the topic of the corpus at the time of reception. Our method considers three major elements: users´s dynamic level of interest in a topic, user´s social relationship such as the number of followers, their real geographical neighborhood, and other explicit features related to the publishers authority and the tweet´s content.