WEB-BASED MOVIE RECOMMENDATION SYSTEM USING CONTENT-BASED FILTERING

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.