Abstract
This paper presents our 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, we parse the textual content of the eBook currently read by the user 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, we identify a set of relevant eResources from the World Wide Web. 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. We strive to obtain an additional wisdom-of-crowd kind of evaluation of our system by hosting it on a public Web platform.
This work is partly supported by an Indo-Mexican project funded jointly by DST, India and CONACYT, Mexico.
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Singh, V.K., Piryani, R., Uddin, A., Pinto, D. (2013). A Content-Based eResource Recommender System to Augment eBook-Based Learning. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_24
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DOI: https://doi.org/10.1007/978-3-642-44949-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-44948-2
Online ISBN: 978-3-642-44949-9
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