Abstract:
Recommendation systems, 'RS', have emerged as a major research subject aimed
at helping users find articles online by providing suggestions that closely match their
interests. In today's modern information technology age, the idea of efficiently finding
your favorite products in large datasets in application databases becomes a key issue for
online content providers to attract the masses unlike their competitors.
The variety of techniques available makes choosing a technique when building an
application-oriented recommender system a complex task. Moreover, each technique has
its own characteristics, strengths and weaknesses, which raises many more questions.
This project aims to work in the area of movie-centric recommendation systems. First,
various data cleansing in the recommendation system. Next, an algorithmic analysis of
the recommendation system is performed. Additionally, performance metrics focused on
the collected dataset, simulation platform, and each post are evaluated and recorded.
