PI: Dr. Hesham El Mongy
Funding Agency:Microsoft ATLC
Duration: 12 Months

Project Abstract

Microblogs are special virtual social network web-based applications. Users of microblogs are allowed to post relatively short messages (corpuses) compared to regular blogs. This encouraged many users to become more active, as the effort they need to put to post a message is very small. On the other hand, 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 every day. In this project, we propose a personalized recommendation system that will aim to give the user a summary of all received corpuses. Considering the fact that the user interests change over time (and location), 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’ 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.