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This project explores the hybrid recommendation challenge by defining key concepts and mapping behavioural and contextual data. It delves into algorithms, explaining collaborative filtering and cosine similarity, while providing worked examples of matrix factorisation. The engineering phase focuses on assembling personalised content and evaluating performance using metrics like RMSE and Precision@K. Finally, it addresses challenges and outlines future directions in AI, ensuring a...