InLinx for Document Classification, Sharing and Recommendation

This paper proposes an hybrid recommender system, InLinx, that combine content analysis and the development of virtual clusters of students and of didactical sources providing facilities to use the huge amount of digital information according to the student’s personal requirements and interests. The paper proposes novel methods for information management, with special focus on the development of new algorithms and intelligent applications for personalized information sharing, filtering and retrieval. InLinx helps the student to classify domain specific information found in the Web and saved as bookmarks, to recommend these documents to other students with similar interests and to periodically notify new potential interesting documents.