A Design of Recommender Systems with Hybrid Models
Academic Article
Overview
Overview
Abstract
The rapid growth of digital media content makes it difficult to provide timely, relevant, and personalized recommendations. In isolation, traditional recommender systems are effective, but they often struggle with data sparsity, the cold-start problem, diversity, and adaptability to changing user preferences. Systems that accurately interpret user behavior and item characteristics are essential. 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, we developed hybrid movie recommendation models by combining collaborative filtering with content-based analysis using NLP and two-tower neural network 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 help to 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. The findings demonstrate the efficacy of selected hybrid strategies for personalized recommendations across similar application domains.
