Indiana University-Purdue University Indianapolis (IUPUI) / This research proposed a dynamic recommendation system for a social learning
environment entitled CourseNetworking (CN). The CN provides an opportunity for
the users to satisfy their academic requirement in which they receive the most relevant and updated content. In our research, we extracted some implicit and explicit
features from the system, which are the most relevant user feature and posts features. The selected features are used to make a rating scale between users and posts
so that represent the link between user and post in this learning management system
(LMS). We developed an algorithm which measures the link between each user and
post for the individual. To achieve our goal in our system design, we applied natural
language processing technique (NLP) for text analysis and applied various classi cation technique with the aim of feature selection. We believe that considering the content
of the posts in learning environments as an impactful feature will greatly affect to
the performance of our system. Our experimental results demonstrated that our recommender system predicts the most informative and relevant posts to the users. Our
system design addressed the sparsity and cold-start problems, which are the two main
challenging issues in recommender systems.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/17002 |
| Date | January 2018 |
| Creators | Mirzaeibonehkhater, Marzieh |
| Contributors | King, Brian, Jafari, Ali, Liu, Hongbo |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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