INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN MULTIDISCIPLINARY EDUCATION ISSN (print): 2833-4515, ISSN (online): 2833-4531 Volume 03 Issue 12 December 2024 DOI: 10.58806/ijirme.2024.v3i12n18, Impact factor- 5.138 Page No. 1972-1980 IJIRME, Volume 3 Issue 12 December 2024 www.ijirme.com Page 1972 Evaluating Machine Learning Approaches for Personalized Movie Recommendations: A Comprehensive Analysis Achuthananda Reddy Polu 1 , Bhumeka Narra 2 , Dheeraj Varun Kumar Reddy Buddula 3 , Hari Hara Sudheer Patchipulusu 4 , Navya Vattikonda 5 , Anuj Kumar Gupta 6 1 Senior SDE, Cloudhub IT Solutions 2 Sr Software Developer, Statefarm 3 Software Engineer, Elevance Health Inc 4 Senior Software Engineer, Walmart 5 Business Intelligence Engineer, International Medical Group Inc 6 Oracle ERP Senior Business Analyst ,Genesis Alkali ABSTRACT: Platforms and movie theatres provide a large range of movies that need to be filtered to each user's tastes. For this objective, recommender systems are a useful tool. This research presents a novel hybrid recommender system for personalized movie suggestions, which integrates content-based methods with collaborative filtering. This study develops a personalized movie recommendation system utilizing the MovieLens 1M dataset, comprising user ratings for a diverse set of movies. The research data undergoes separation into training segments that constitute 80% of the total sample while testing comprises 20% of the data. The evaluation framework incorporates multiple metrics which include F1-Score together with Precision and Root Mean Square Error (RMSE) as well as Mean Absolute Error (MAE) alongside Recall. A Multilayer Perceptron (MLP) model is employed for movie recommendations and compared to a Deep Neural Network (DNN). The outcomes show that a MLP model outperforms the DNN, achieving a lower RMSE of 0.99 and an MAE of 0.80, in contrast to the DNN’s RMSE of 1.011 and MAE of 73.5. The training and validation loss trajectories show continuous progress and maintain minimal avoidable patterns. Future work will refine the model accuracy by performing hyperparameter tweaking and developing advanced feature extraction processes together with implementing distinct deep learning models for enhanced recommendation capability and user satisfaction. KEYWORDS: Personalized Movie Recommendation, Movie Classification, Recommendation Systems, User Preferences, Machine Learning (ML), Movie Lens 1M data. I. INTRODUCTION Individualized recommendation functions holistically influence user experiences on multiple digital platforms across the entertainment sector and e-commerce platforms. Modern content discovery systems rely on personalized movie recommendations to become the cornerstone feature that helps users efficiently discover content from massive databases. Customized entertainment experiences emerge from systems that generate content recommendations using viewer historical behaviors along with rating histories and their preferred genres [1]. The rapid expansion of online content demands efficient recommendation systems for users to navigate an endless sea of available content toward personalized choices. Types of custom recommendation systems employ machine learning (ML) algorithms to explore user feedback patterns alongside content properties within large datasets to develop precise recommendation options[2]. Users may now go through the vast amount of information on the internet and locate just the content that suits them due to movie recommender systems. Through content-based filtering and collaborative filtering alongside hybrid models these systems present personalized recommendations that increase user satisfaction. Users benefit from two types of recommendations based on collaborative filtering which discovers user behavior patterns to suggest content that matches other users' preferences alongside content-based filtering that recommends relevant past-viewed content[3]. Hybrid models utilize various recommendation techniques to produce more efficient results by resolving individual implementation barriers that each technique possesses. The fundamental need for evaluating machine learning approaches in movie recommendation emerged due to fast technological progress alongside evolving user taste patterns[4]. Researchers investigate how different algorithms process extensive datasets while generating accurate suggestion results. The research examines different machine-learning approaches for movie recommendation systems while assessing their performance benefits and constraints for actual system deployment [5]. This research evaluates multiple