Building LLM Powered Applications by Valentina Alto

Author:Valentina Alto
Language: eng
Format: epub
Tags: COM058000 - COMPUTERS / Desktop Applications / Word Processing, COM044000 - COMPUTERS / Neural Networks, COM042000 - COMPUTERS / Natural Language Processing
Publisher: Packt
Published: 2024-05-15T06:02:43+00:00

The following is the output:

Reconstructed Matrix: [[4.2972542 0. 4.71897811 0. ] [1.08572801 2.27604748 0. 1.64449028] [4.44777253 4.36821972 0.52207171 3.18082082]]

In this example, the U matrix contains user-related information, the s matrix contains singular values, and the V matrix contains movie-related information. By selecting a certain number of latent factors (num_latent_factors), you can reconstruct the original matrix with reduced dimensions, while setting the full_matrices=False parameter in the np.linalg.svd function ensures that the decomposed matrices are truncated to have dimensions consistent with the selected number of latent factors.

These predicted ratings can then be used to recommend movies with higher predicted ratings to users. Matrix factorization enables recommendation systems to uncover hidden patterns in user preferences and make personalized recommendations based on those patterns.

Matrix factorization has been a widely used technique in recommendation systems, especially when dealing with large datasets containing a substantial number of users and items, since it efficiently captures latent factors even in such scenarios; or when you want personalized recommendations based on latent factors, since it learns unique latent representations for each user and item. However, it has some pitfalls (some similar to the KNN’s technique):

Cold-start problem: Similar to KNN, matrix factorization struggles with new items or users that have limited or no interaction history. Since it relies on historical data, it can’t effectively provide recommendations for new items or users.

Data sparsity: As the number of users and items grows, the user-item interaction matrix becomes increasingly sparse, leading to challenges in accurately predicting missing values.

Scalability: For large datasets, performing matrix factorization can be computationally expensive and time-consuming.

Limited context: Matrix factorization typically only considers user-item interactions, ignoring contextual information like time, location, or additional user attributes.

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