ScholarWorksIndianapolis
  • Communities & Collections
  • Browse ScholarWorks
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Subject

Browsing by Subject "Recommendation systems"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Developing a dynamic recommendation system for personalizing educational content within an E-learning network
    (2018) Mirzaeibonehkhater, Marzieh; King, Brian; Jafari, Ali; Liu, Hongbo
    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.
About IU Indianapolis ScholarWorks
  • Accessibility
  • Privacy Notice
  • Copyright © 2025 The Trustees of Indiana University