An In-Depth Strategy using Deep Generative Adversarial Networks for Addressing the Cold Start in Movie Recommendation Systems

This study addresses the intricate cold start challenge in movie recommendation systems (RSs) by integrating Collaborative Filtering (CF) and Singular Value Decomposition (SVD), complemented by Generative Adversarial Networks (GAN). The cold start problem arises when the system is unable to provide accurate recommendations for new users or items with limited interaction history. The addition of Content-Based (CB) filtering enhances outcome depth, while thorough data exploration and meticulous feature engineering, including extracting details like release years, enrich the dataset for a nuanced understanding of user preferences. The unique strength lies in the synergistic application of CF-SVD, GAN, and CB filtering, providing a multifaceted strategy to overcome the cold start challenge. Results, guided by strategic CB filtering, offer comprehensive insights, emphasizing the pivotal role of content in crafting accurate and personalized movie recommendations. The innovative integration establishes our approach as a robust solution, proficient in providing nuanced suggestions, especially in scenarios with limited user interaction data, thereby advancing the efficacy of movie RSs. GAN, a state-of-the-art framework, contributes significantly to our integrated RSs by leveraging a generator model to create synthetic data, refining CF outcomes. This contribution is evaluated using the MovieLens dataset, encompassing ratings and movie information.