Curator: Enhancing micro-blogs ranking by exploiting user’s context

Abstract

Micro-blogging services have emerged as a powerful, real-time, way to disseminate information on the web. A small fraction of the colossal volume of posts overall are relevant. We propose Curator, a micro-blogging recommendation system that ranks micro-blogs appearing on a user’s timeline according to her context. Curator learns user’s time variant preferences from the text of the micro-blogs the user interacts with. Furthermore, Curator infers the user’s home location and the micro-blog’s subject location with the help of textual features. Precisely, we analyze the user’s context dynamically from the micro-blogs and rank them accordingly by using a set of machine learning and natural language processing techniques. Curators extensive performance evaluation on a publicly available dataset show that it outperforms the competitive state-of-the-art by up to 154% on NDCG@5 and 105% on NDCG@25. The results also show that location is a salient feature in Curator.