Integrating Transformers into Recommendation Systems: A Hybrid Approach

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In recent years, recommendation systems have become essential for various industries, from e-commerce to social media. This paper explores the integration of Transformer models within recommendation systems, which enhances the model's ability to capture long-range dependencies in user interactions. We present a hybrid recommendation approach combining collaborative filtering and Transformer-based content analysis. A case study is included, demonstrating how this integration handles both cold-start problems and typical user-item recommendation tasks.