Micro-blogging systems have become a prime source of information. However due to their unprecedented success, these systems have to face an exponentially increasing amount of user generated content. As a consequence, finding users who publish quality content that matches precise interests is a real challenge for the average user. We present in this article a recommendation score which takes advantage of the social graph topology and of the existing contextual information to recommend users to follow on a given topic. We also introduce a landmark-based algorithm that precomputes recommendation scores for a given set of graph nodes and that allows to scale. Our experimental results confirm the relevance of this score against existent approaches as well as the scalability of our landmark-based algorithm.
recommendation, social networks, micro-blogging
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