Abstract

The rapid growth of digital media content has led to significant challenges in delivering timely, relevant, and personalized recommendations to users. Traditional recommender systems, while effective in isolated contexts, often struggle with issues such as data sparsity, the cold-start problem, lack of diversity, and limited adaptability to evolving user preferences. Particularly in the domain of movie recommendation, the need for systems that can accurately interpret both user behavior and item characteristics has become critical. Addressing these challenges requires strategies that go beyond conventional collaborative or content-based filtering alone. This study aims to address the following research question: How can hybrid approaches integrating traditional recommendation techniques with modern machine learning methods improve the personalization, diversity, and resilience of a recommender system? To this end, hybrid models for movie recommendations were created by combining classical collaborative filtering with content-based analysis utilizing natural language processing and neural networks based on two-tower model architectures. The collaborative filtering components utilize matrix factorization to uncover latent user preferences, while natural language processing techniques extract semantic features from movie descriptions to enhance content understanding. Neural retrieval-ranking models further refine recommendations by learning compact representations of users and items, enabling efficient and adaptive candidate selection. The evaluation methodology included both offline algorithmic performance measurement and user-centered assessments to capture real-world recommendation effectiveness. This study proposes a framework for creating robust, scalable, effective, manipulation-resistant and user-centric recommender systems (Burke, 2002; Konstan & Riedl, 2012). The findings of this research prove the effectiveness of selected hybrid strategies for personalized recommendations and provide an analysis that may inform future model selection in similar contexts.

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