We present a recommendation system for social media that draws upon monitoring and prediction methods. We use historical posts on some focal topic or historical links to a focal blog channel to recommend a set of authors to follow. Such a system would be useful for brand managers interested in monitoring conversations about their products. Our recommendations are based on a prediction system that trains a ranking Support Vector Machine (RSVM) using multiple features including the content of a post, similarity between posts, links between posts and/or blog channels, and links to external websites. We solve two problems, Future Author Prediction (FAP) and Future Link Prediction (FLP), and apply the prediction outcome to make recommendations. Using an extensive experimental evaluation on a blog dataset, we demonstrate the quality and value of our recommendations.
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