This work presents a recommender system of economic news articles. Its objectives are threefold: (ⅰ) managing the vocabulary of the economic news domain to improve the system based on the seamlessly intervention of the documentalist (ⅱ) automatically multi-classify the economic new articles and users profiles based on the domain vocabulary, and (ⅲ) recommend the articles by comparing the multi-classification of the articles and profiles of the users. While several solutions exist to recommend news, multi-classify document and compare representations of items and profiles. They are not automatically adaptable to provide a mutual answer to previous points. Even more, existing approaches lacks substantial correlation with the human and in particular with the documentalist perspective.
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