METIORE: A Publications Reference for the Adaptive Hypermedia Community The Web is one of the most powerful sources of information about any topic. However its disorganization makes it difficult to find even existing. Look for scientific literature is also a difficult task. Itu0027s needed to know some places that links to the source of information, and in the best case they point to conferences proceedings. If this is the case the user is not able to make queries about the available documents and must check them one by one. In this paper we propose our system METIORE as a source of information for the Adaptive Hypermedia community. The idea is to keep together all the publications on this research area and provide a powerful tool to find papers or people working in the field. METIORE is a Personalized Information Retrieval system that keeps a user model based on objectives

HierCon: Hierarchical Organization of Technical Documents Based on Concepts In this work we study the hierarchical organization of technical documents, where given a set of documents and a hierarchy of categories, the goal is to assign documents to their corresponding categories. Unlike prior work on supervised hierarchical document categorization that relies on large amount of labeled training data, which is expensive to obtain in closed technical domain and tends to stale as new knowledge emerges, we study this problem in a weak supervision setting, by leveraging semantic information from concepts. The core idea is to project both documents and categories into a common concept embedding space, where their finegrained similarity can be easily and effectively computed. Experiments over realworld datasets from the subject of computer science, physics u0026 mathematics, and medicine demonstrated the superior performance of our approach over a wide range of state of the art baseline approaches.