Hybrid Content-Collaborative Filtering: Improving Collaborative Filtering and Neural Network-based Recommendation

Abstract

Effective recommendation systems have become increasingly important in the current period, which is characterised by an exponential increase in the amount of content available. These systems are essential for guiding users through a sea of possibilities by providing recommendations that are tailored to their tastes. Given the vast volume of content on streaming services and internet databases, movie suggestions are especially important. A new era of precise and customised movie recommendations has arrived thanks to sophisticated algorithms that have arisen as a result of technological advancements. By contrasting them with conventional collaborative filtering techniques, this study examines the potential of deep learning techniques in the field of movie recommendation. While deep learning approaches are adept at recommendation jobs, collaborative filtering uses user-item interactions to make predictions from unstructured data. The purpose of the study is to determine whether collaborative filtering's limitations, particularly for new users and objects, can be overcome using deep learning. In order to determine whether deep learning may improve accuracy and circumvent problems associated with conventional approaches, the research probes the complex dynamics of deep learning in movie recommendations. It looks at how deep learning's capacity to find latent patterns can produce more precise and nuanced recommendations. The broad objectives of the research include a comparative performance analysis, an investigation of strengths and shortcomings, and useful insights for model construction.