A Comparison Study for Novelty Control Mechanisms Applied to Web News Stories

In this paper we evaluate several novelty control mechanisms for ranking Web news articles depicting a story. These mechanisms rank individual news items based on a novelty measure using as context the items which have been previously reviewed. An evaluation within the Hermes news personalization framework is performed for pair wise and non-pair wise novelty control mechanisms based on various distance measures and vector-based news representations. On average, the most effective distance measure is Kullback-Leibler and the best performing news representation vector uses named entities.