Twitter message recommendation based on user interest profiles

Raheleh Makki, Axel J. Soto, Stephen Brooks, Evangelos E. Milios
2016 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)  
The rapid growth of Twitter has made it one of the most popular information sources of current affairs. Twitter users gather information about their topics of interest through their followees' posts or by searching for relevant posts. However, users are often overwhelmed by the large number of tweets which makes it difficult for them to find relevant and non-redundant information about their interests. Information filtering and recommender systems can help users by suggesting informative tweets
more » ... based on their interests. Considering the wide variety of topics in users' interest profiles and the sheer volume of tweets being published daily, it is difficult to have adequate and proper labeled data to train these systems on. We aim to tackle this problem by integrating active learning techniques into tweet recommendation, more specifically for finding relevant tweets and ranking them. Using active learning methods in the context of Twitter recommenders has not been well explored before. Our objective is to exploit these methods for improving the accuracy of tweet recommenders the most, while keeping the cost of labeling to a minimum.
doi:10.1109/asonam.2016.7752266 dblp:conf/asunam/MakkiSBM16 fatcat:3jrug3ieu5ab3byukap3phurum