Tweet and followee personalized recommendations based on knowledge graphs

Twitter users get the latest tweets of their followees on their timeline. However, they are often overwhelmed by the large number of tweets, which makes it difficult for them to find interesting information among them. In this work, we present an efficient semantic recommendation method that helps users filter the Twitter stream for interesting content. The foundation of this method is a knowledge graph (KG) that can represent all user topics of interest as a variety of concepts, objects, events, persons, entities, locations and the relations between them. Our method uses the KG and graph theory algorithms not yet applied in social network analysis in order to construct user interest profiles by retrieving semantic information from tweets. Next, it produces ranked tweet recommendations. In addition, we use the KG to calculate interest similarity between users, and we present a followee recommender based on the same underlying principles. An important advantage of our method is that it reduces the effects of problems such as over-recommendation and over-specialization. As another advantage, our method is not impaired by the limitations posed by Twitter on the availability of the user graph data. We implemented from scratch the best-known state-of-the-art approaches in order to compare with them and assess our method. Moreover, we evaluate the efficiency and runtime scalability of our method.