Personalized Microblogs Corpus Recommendation Based on Dynamic Users Interests

Abstract

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.