Combining Content Analytics and Activity Tracking to Identify User Interests and Enable Knowledge Discovery

Abstract : Finding relevant content is one of the core activities of users interacting with a content repository, be it knowledge workers using an organizational knowledge management system at a workplace or self-regulated learners collaborating in a learning environment. Due to the number of content items stored in such repositories potentially reaching millions or more, and quickly increasing, for the user it can be challenging to find relevant content by browsing or relying on the available search engine. In this paper, we propose to address the problem by providing content and people recommendations based on user interests, enabling relevant knowledge discovery. To build a user interests profile automatically, we propose an approach combining content analytics and activity tracking. We have implemented the recommender system in Graasp, a knowledge management system employed in educational and humanitarian domains. The conducted preliminary evaluation demonstrated an ability of the approach to identify interests relevant to the user and to recommend relevant content.
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https://telearn.archives-ouvertes.fr/hal-01398999
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Submitted on : Friday, November 18, 2016 - 10:42:10 AM
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Andrii Vozniuk, María Jesús Rodríguez-Triana, Adrian Holzer, Denis Gillet. Combining Content Analytics and Activity Tracking to Identify User Interests and Enable Knowledge Discovery. [Research Report] Go-Lab Project. 2016. ⟨hal-01398999⟩

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