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cosine-similarity-scores

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This project uses machine learning to create a personalized bookrecommendation system. By combining collaborative filtering and content-based filtering, it analyzes user preferences and book attributes to suggest tailored book recommendations. The system offers real-time updates and accurate predictions to enhance the user experience.

  • Updated Aug 4, 2024
  • Jupyter Notebook

This is a speaker verification system uses Total Variability and Projection Matrix. Intersession variability was compensated by using backend procedures, such as linear discriminant analysis (LDA) and within-class covariance normalization (WCCN), followed by a scoring, the cosine similarity score. In literature this approach named i-vectors.

  • Updated Dec 23, 2023
  • C++

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