Enhanced Movie Recommendation Systems: Integrating Collaborative Filtering with Content-Based Approaches for Improved User Experience

Authors

Tirth Bharucha, Khushbunaz Dalal, Ayush Shah

Abstract

The exponential growth in digital content consumption has increased the demand for efficient recommendation systems that personalize user experiences. This research explores various techniques for analyzing customer behavio r in movie recommendation systems, integrating collaborative filtering with content-based approaches to improve recommendation accuracy. Initially, user similarity and neighborhood-based filtering methods are employed to generate preliminary recommendations. These are then enhanced by incorporating metadata attributes such as genre, cast, and production details, enabling a more comprehensive and context-aware recommendation process.
The proposed hybrid approach leverages machine learning algorithms, including classification techniques, to categorize and refine recommendations based on user preferences. The study evaluates the impact of preprocessing methodologies to handle missing or incomplete data, ensuring that the recommendation engine operates effectively despite data sparsity challenges. Furthermore, the research addresses limitations in traditional recommendation methods by integrating demographic filtering and similarity metrics such as cosine similarity to optimize recommendation quality.
The analysis also considers the psychological aspects of consumer behavior, demonstrating how an improved recommendation system can contribute to more strategic marketing decisions. Companies such as Netflix, Amazon, and YouTube heavily rely on similar techniques to enhance user engagement and satisfaction. By adopting a hybrid recommendation approach, businesses can enhance recommendation precision while reducing execution time compared to standalone content-based or collaborative filtering methods.
Empirical results suggest that a combination of these techniques improves the accuracy and relevance of recommendations. Additionally, future advancements in hybrid recommendation models could further refine prediction capabilities by incorporating deep learning methods and sentiment analysis from user reviews. This research highlights the importance of personalized recommendation engines in modern digital platforms, underscoring their role in enhancing user engagement and business profitability.

Keywords

Recommendation Systems, Hybrid Approach, Collaborative Filtering, Content-Based Filtering, Machine Learning, Cosine Similarity, Demographic Filtering, User Behavior Analysis, Metadata Integration, Personalization, Data Sparsity, Psychological Aspects, Deep Learning, Sentiment Analysis, Digital Content Consumption.

Enhanced Movie Recommendation Systems: Integrating Collaborative Filtering with Content-Based Approaches for Improved User Experience. Tirth Bharucha, Khushbunaz Dalal, Ayush Shah. 2025. IJIRCT, Volume 11, Issue 1. Pages 1-14. https://www.ijirct.org/viewPaper.php?paperId=2502077