Publication Date

Fall 2019

Advisor(s) - Committee Chair

Dr. Huanjing Wang (Director), Dr. Guangming Xing, Dr. Zhonghang Xia

Degree Program

School of Engineering and Applied Sciences

Degree Type

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