User-Oriented Content Retrieval Using Image Segmentation Techniques

Abstract

The need for applying advanced social information retrieval techniques for personalizing web-based information discovery has been identified as a key challenge. Until now, significant R&D effort has been devoted aiming towards applying collaborative filtering techniques for educational content retrieval. However, limited attention has been given to the use of educational metadata as a mean to enhance social filtering techniques via educationally informed filtering decisions. In this paper we propose the use of an add-on filtering service on existing social filtering systems/applications so as to create a data post-filtering mechanism that makes use of intelligence stored in TEL metadata. The proposed methodology starts with the generation of a matrix that represents the educational characteristics of the resources suggested by typical social filtering techniques and applies post-filtering using the educational “footprint” of the resources already used by the targeted end-user.