Published Paper Details:

In today's digital landscape, recommendation systems play a pivotal role in enhancing user experiences and driving business success. This research delves into the intricate workings of recommendation systems, with a particular focus on the Movie Recommendation System using Machine Learning and Natural Language Processing (NLP). It investigates three core recommendation techniques: Demographic Filtering, Content-Based Filtering, and Collaborative Filtering. Demographic Filtering leverages user characteristics such as age, gender, and location, often in conjunction with item attributes like image and genre, to provide tailored recommendations. Content-Based Filtering takes a granular view by analyzing detailed item attributes, such as director, actors, and content themes, aiming to offer personalized suggestions based on user preferences. Collaborative Filtering, the third technique, harnesses collective user behavior, including strategies like Singular Value Decomposition (SVD) and item-based collaborative filtering, to uncover hidden patterns and make recommendations. Throughout this research, empirical evidence is presented to illustrate the effectiveness of these recommendation techniques in elevating user satisfaction and engagement. The study underscores the need for a balanced approach to accommodate diverse user preferences and provide accurate, appealing recommendations in today's highly competitive digital landscape.