Date of Award


Document Type


Degree Name

Master of Science in Computer Science


School of Computer Science and Engineering

First Reader/Committee Chair

Zhang, Yan


In today’s age of streaming services, the effectiveness and precision of recommendation systems are crucial in improving user satisfaction. This project introduces the Smart Hybrid Enhanced Recommendation and Personalization Algorithm (SHERPA) a cutting-edge machine learning approach aimed at transforming how movie suggestions are made. By combining Term Frequency Inverse Document Frequency (TF-IDF) for content based filtering and Alternating Squares (ALS) with Weighted Regularization for filtering SHERPA offers a sophisticated method for delivering tailored recommendations.

The algorithm underwent evaluation using a dataset that included over 50 million ratings from 480,000 Netflix users encompassing 17,000 movie titles. The performance of SHERPA was meticulously compared to traditional hybrid models demonstrating a 70% enhancement in prediction accuracy based on Root Mean Square Error (RMSE) metrics during training, testing and validation phases.

These findings highlight SHERPAs capability to understand and cater to users’ subtle preferences representing an advancement in personalized recommendation systems.