Trend-Aware User Modeling with Location-Aware Trends on Twitter

Microblogs are a phenomenon of modern social media. As there is much real-time social information in there, they are candidates to be used as a source for mining important information enhancing user experience in variety of web applications, especially those related with content adaptation and recommendation. In this paper we deal with microblog-based user models. We propose trend-aware user model with location-aware trends, which focuses on location aspects and trends. It is a general model, which can be used in various domains. We evaluated the model in a domain of news recommendations and we showed that recommendation based on this model outperforms state-of-the-art approaches.