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 (Department of Computer Science, Babasaheb Bhimrao Ambedkar University (BBAU), Lucknow, India) and Sanjay K. Dwivedi (Department of Computer Science, Babasaheb Bhimrao Ambedkar University (BBAU), Lucknow, India)
DOI: 10.4018/IJAPUC.2018010104

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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Type Of Recommendation Approaches

We have broadly categorized recommendation techniques into “traditional techniques” and “non-traditional techniques” as shown in Figure 1. Unlike earlier survey papers on recommendation systems, this paper also includes web usage mining (WUM) and semantic web usage mining (SWUM) which play a major role for developing next generation of recommender systems.

Figure 1.

Types of recommendation approaches with their domains

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