Finding satisfactory scientific literature is still a very time-consuming task. In the last decade several tools have been proposed to approach this task, however only few of them actually analyse the whole document in order to select and present it to the user and even less tools offer any kind of explanation of why a given item was retrieved/recommended. The main goal of this demonstration is to present the RES system, a tool intended to overcome the limitations of traditional recommender and personalized information retrieval systems by exploiting a more semantic approach where concepts are extracted from the papers in order to generate and then explain the recommendar tion. RES acts like a personalized interface for the well-known CiteSeerX system, filtering and presenting query results accordingly to individual user's interests.
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