Abstract
Media monitoring services allow their customers, mostly companies, to receive, on a daily basis, a list of documents from mass media that discuss topics relevant to the company. However, media monitoring services often generate these lists by using keyword-filtering techniques, which introduce many false positives. Hence, before the end users, i.e., the employees of the company, may consult these lists and find relevant documents, a human editor must inspect the keyword-filtered documents and remove the false positives. This is a time consuming job. In this paper we present a recommender system that aims at reducing the number of documents that the editor needs to inspect every day. The proposed solution classifies documents (represented with TF-IDF and embeddings features) using techniques trained on data containing the editors’ past actions (i.e. the removals of false positives). The proposed technique is shown to be able to correctly predict the true positives, thus reducing the number of documents that the editor needs to inspect every day.