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State of the Art Recommendation Approaches: Their Issues and Future Research Direction in E-Learning A Survey

State of the Art Recommendation Approaches: Their Issues and Future Research Direction in E-Learning A Survey

Bhupesh Rawat, Sanjay K. Dwivedi
Copyright: © 2018 |Volume: 10 |Issue: 1 |Pages: 26
ISSN: 1937-965X|EISSN: 1937-9668|EISBN13: 9781522543404|DOI: 10.4018/IJAPUC.2018010104
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MLA

Rawat, Bhupesh, and Sanjay K. Dwivedi. "State of the Art Recommendation Approaches: Their Issues and Future Research Direction in E-Learning A Survey." IJAPUC vol.10, no.1 2018: pp.51-76. http://doi.org/10.4018/IJAPUC.2018010104

APA

Rawat, B. & Dwivedi, S. K. (2018). State of the Art Recommendation Approaches: Their Issues and Future Research Direction in E-Learning A Survey. International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), 10(1), 51-76. http://doi.org/10.4018/IJAPUC.2018010104

Chicago

Rawat, Bhupesh, and Sanjay K. Dwivedi. "State of the Art Recommendation Approaches: Their Issues and Future Research Direction in E-Learning A Survey," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC) 10, no.1: 51-76. http://doi.org/10.4018/IJAPUC.2018010104

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Abstract

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.

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