Interaction-based Collaborative Recommendation: A Personalized Learning Environment (PLE) Perspective
Syed Mubarak Ali, Imran Ghani, Muhammad Shafie Abd Latiff · KSII Transactions on Internet and Information Systems · 2015
In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies.There are different variations or classification of e-learning approaches.One of notable approaches is Personal Learning Environment (PLE).In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants).The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately.This phenomenon is known as cold-start problem.In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations.In this research paper, the implementation of a novel interaction-based approach is presented.The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system.This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem.The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.