Emad_Rahmaniazad_Thesis.pdf (7.3 MB)

Community Recommendation in Social Networks with Sparse Data

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thesis
posted on 07.01.2021, 15:56 by Emad Rahmaniazad
Recommender systems are widely used in many domains. In this work, the importance of a recommender system in an online learning platform is discussed. After explaining the concept of adding an intelligent agent to online education systems, some features of the Course Networking (CN) website are demonstrated. Finally, the relation between CN, the intelligent agent (Rumi), and the recommender system is presented. Along with the argument of three different approaches for building a community recommendation system. The result shows that the Neighboring Collaborative Filtering (NCF) outperforms both the transfer learning method and the Continuous bag-of-words approach. The NCF algorithm has a general format with two various implementations that can be used for other recommendations, such as course, skill, major, and book recommendations.

History

Degree Type

Master of Science in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Campus location

Indianapolis

Advisor/Supervisor/Committee Chair

Dr. Brian King

Advisor/Supervisor/Committee co-chair

Dr. Ali Jafari

Additional Committee Member 2

Dr. Paul Salama