Building A Hybrid Recommender System

The rise of the Internet and its extensive use has led to content oversaturation, resulting in content oversaturation, and complicating the effective delivery of relevant and interesting information to users. Recommendation systems help mitigate this by filtering and prioritizing content based on user data, enabling personalized experiences on streaming and e-commerce platforms. This paper reviews the main methodologies for constructing and assessing recommendation system and demonstrates the issues of existing approaches. To address the challenges, this study proposes a solution, using a hybrid filtering model to incorporate classical filtering models with machine learning techniques. This research will show how to build, train, test, and utilize a movie recommendation system based on real-world data.

Building A Hybrid Recommender System Academic Article