HELP: A Recommender System to Locate Expertise in Organizational Memories

The rapid evolution of our world means that learning and knowledge sharing are fast becoming a key challenge for individuals and organizations. In this paper, we present a system called HELP, whose aim is to locate information and recommend experts in organizations. Each user is being viewed simultaneously as an expert and a learner. We use two approaches: The first one consists of making the system retrieve one or several requests similar to the seeking-learner's request using a textual case-based reasoning technique. The second approach aims at locating experts in specific areas in order to recommend them to the users who request this expertise. For this purpose, we use a hybrid recommendation technique based on Collaborative Filtering (CF) and Case-Based Reasoning (CBR). In contrast to existing approaches in expertise location, we believe that CBR combined to CF enables HELP to better recommend expertise, taking into account the user's feedback concerning the technical and pedagogical skills of the experts.

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