A Learning Context-Based E-Resource Recommendation System

This paper presents experimental work to design a content-based recommendation system for eBook readers. The system automatically identifies a set of relevant eResources for a reader, reading a particular eBook, and presents them to the user through an integrated interface. The system involves two different phases. In the first phase, the textual content of the eBook currently read by the user is parsed to identify learning concepts being pursued. This requires analysing the text of relevant part(s) of the eBook to extract concepts and subsequently filter them to identify learning concepts of interest to Computer Science domain. In the second phase, a set of relevant eResources from the World Wide Web are identified and presented to reader. This involves invoking publicly available APIs from Slideshare, LinkedIn, YouTube etc. to retrieve relevant eResources for the learning concepts identified in the first part. The system is evaluated through a multi-faceted process involving tasks like sentiment analysis of user reviews of the retrieved set of eResources for recommendations.