Advisor(s) - Committee Chair
Dr. Huanjing Wang (Director), Dr. Guangming Xing, Dr. Zhonghang Xia
School of Engineering and Applied Sciences
Master of Science
Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation system
Computer Sciences | Engineering
Lokesh, Ashwini, "A Comparative Study of Recommendation Systems" (2019). Masters Theses & Specialist Projects. Paper 3166.