Affiliations: [a]
University College of Engineering, Panruti, Tamilnadu, India
| [b]
University College of Engineering, Ariyalur, Tamilnadu, India
| [c]
Centre for Cyber-Physical Systems, Vellore Institute of Technology, Chennai, Tamilnadu, India
| [d]
Sethu Institute of Technology, Virudhunagar, Tamilnadu, India
Correspondence:
[*]
Corresponding author. Ramachandran Alagarsamy, University College of Engineering, Panruti, Tamilnadu, India. E-mail: [email protected].
Abstract: Recently, the e-learners are drastically increased from the last two decades. Everything is learnt through internet without help of the tutor as well. For this purpose, the e-learners are required more e-learning applications that are able to supply optimal and satisfied data based on their capability. No content recommendation system is available for recommending suitable contents to the learners. For this purpose, this paper proposes a new semantic and fuzzy aware content recommendation system for retrieving the suitable content for the users. In this content recommendation system, we propose two content pre-processing algorithms namely Target Keyword based Data Pre-processing Algorithm (TKDPA) and Intelligent Anova-T Residual Algorithm (IAATRA) for selecting the more relevant features from the document. Moreover, a new Fuzzy rule based Similarity Matching algorithm (FRSMA) is proposed and used in this system for finding the similarity between the two terms and also rank them by using the newly proposed Similarity and Temporal aware Weighted Document Ranking Algorithm (STWDRA). In addition, a content clustering process is also incorporated for gathering relevant content. Finally, a new Fuzzy, Target Keyword and Similarity Score based Content Recommendation Algorithm (FTKSCRA) is also proposed for recommending the more relevant content to the learners accurately. The experiments have been conducted for evaluating the proposed content recommendation system and proved as better than the existing recommendation systems in terms of precision, recall, f-measure and prediction accuracy.
